### Syllabus

## ML.Syllabus History

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* Reading: [[http://people.physics.anu.edu.au/~tas110/Teaching/Lectures/L3/Material/Myung03.pdf | Tutorial on MLE estimation]] ~~ '''quiz'''~~

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* Reading: [[http://people.physics.anu.edu.au/~tas110/Teaching/Lectures/L3/Material/Myung03.pdf | Tutorial on MLE estimation]]

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* Readings: Mitchell, Chapter ~~3~~

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* Readings: Mitchell, Chapter 3 '''quiz'''

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* ~~Readings~~: Goodfellow, Chapter ~~3~~

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* Reading: Goodfellow, Chapter 3 '''quiz'''

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* ~~Readings~~: ~~Bishop, Chapters 1 and 2~~

*[[http://people.physics.anu.edu.au/~tas110/Teaching/Lectures/L3/Material/Myung03.pdf | Tutorial on MLE estimation]]

*

to:

* Reading: [[http://people.physics.anu.edu.au/~tas110/Teaching/Lectures/L3/Material/Myung03.pdf | Tutorial on MLE estimation]] '''quiz'''

* Optional: Bishop, Chapters 1 and 2

* Optional: Bishop, Chapters 1 and 2

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* Readings: Tom Mitchell's book chapter http://www.cs.cmu.edu/~tom/mlbook/NBayesLogReg.pdf

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* Readings: Tom Mitchell's book chapter [[http://www.cs.cmu.edu/~tom/mlbook/NBayesLogReg.pdf]] '''quiz'''

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* Tom Mitchell's http://www.cs.cmu.edu/~tom/mlbook/NBayesLogReg.pdf.

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* Tom Mitchell's http://www.cs.cmu.edu/~tom/mlbook/NBayesLogReg.pdf. '''quiz'''

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* ~~Readings~~: Bishop Chapter 6

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* Optional: Bishop Chapter 6

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* ~~Readings~~: Bishop, Chapter 7

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* Optional: Bishop, Chapter 7

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You can expect to have simple exercises every meeting. These daily assignments will be done in groups specified by the instructor and they will account for your participation grade (~~10~~% of your final grade)

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You can expect to have simple exercises every meeting. These daily assignments will be done in groups specified by the instructor and they will account for your participation grade (15% of your final grade)

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* Please refer to piazza for any and all communication in the course

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* Projects can be made in teams as follows:

** 2 graduate students

** 1 graduate student + 2 undergraduate students

** 4 undergraduate students

** 2 graduate students

** 1 graduate student + 2 undergraduate students

** 4 undergraduate students

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* Undergraduate students will be graded on 80% of the exam, graduate studens will be graded on 100% of the exam

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Readings in this syllabus are mandatory, you can expect short individual quizzes every week. Those quizzes will be graded and they account for ~~10~~% of your final grade. Quizzes are done first thing in the morning if you are late 5 minutes that may mean that you missed the quiz. If you have an excused absence and missed a quiz, you can make up for it by writing a 2-page summary of the reading. If your absence is unexcused, you cannot make up that quiz.

to:

Readings in this syllabus are mandatory, you can expect short individual quizzes every week. Those quizzes will be graded and they account for 15% of your final grade. Quizzes are done first thing in the morning if you are late 5 minutes that may mean that you missed the quiz. If you have an excused absence and missed a quiz, you can make up for it by writing a 2-page summary of the reading. If your absence is unexcused, you cannot make up that quiz.

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* Readings: Bishop, Chapters 1 and 2

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* Readings: Goodfellow, Chapter 3

* Optional readings: Bishop, Chapters 1 and 2

* Optional readings: Bishop, Chapters 1 and 2

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!! FEEDBACK

I value student's opinions regarding the course and I will take them into consideration to make this course as exciting and engaging as possible. Thus, through the semester I will ask students formal and informal feedback. Formal feedback includes short surveys on my teaching effectiveness, preferred teaching methods, and the pace of the class. Informal feedback will be in the form of polls or in-class questions regarding learning preferences. You can also leave anonymous feedback in the form of a note in my departmental mailbox, under my office door, or %target=_blank%[[https://docs.google.com/spreadsheet/viewform?fromEmail=true&formkey=dHZvUElpWXJsTXV6RTAwZGlCTkYtQVE6MQ | using this form]]. Remember that it is in the best interest of the class if you bring up to my attention if something is not working properly (e.g the pace of the class is too slow, the projects are boring, my teaching style is not effective) so that I can make the corrective steps.

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----------

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!! FEEDBACK

I value student's opinions regarding the course and I will take them into consideration to make this course as exciting and engaging as possible. Thus, through the semester I will ask students formal and informal feedback. Formal feedback includes short surveys on my teaching effectiveness, preferred teaching methods, and the pace of the class. Informal feedback will be in the form of polls or in-class questions regarding learning preferences. You can also leave anonymous feedback in the form of a note in my departmental mailbox, under my office door, or %target=_blank%[[https://docs.google.com/spreadsheet/viewform?fromEmail=true&formkey=dHZvUElpWXJsTXV6RTAwZGlCTkYtQVE6MQ | using this form]]. Remember that it is in the best interest of the class if you bring up to my attention if something is not working properly (e.g the pace of the class is too slow, the projects are boring, my teaching style is not effective) so that I can make the corrective steps.

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'''ONLINE''' The online section is '''remote synchronous''' which means that students have to be online during lecture time. Since participation and questions are crucial for this course, your camera has to be on at all times.

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'''Excused absences have to be notified to the TA and instructor at least 24hrs in advance, sickness has to be justified with a doctor's note'''

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* Project reports ~~have to be uploaded~~ to ~~UNM Learn, code and~~ solution ~~have~~ to be uploaded through Kaggle. Late projects will be accepted only within the following policy:

to:

* Project reports and code (No datasets) have to be uploaded to UNM Learn, solution key has to be uploaded through Kaggle. Late projects will be accepted only within the following policy:

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!! '''[[BigData.IntroToMachineLearning | Course Information ]]'''

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*

* [[https

*

* [[https://www.slideshare.net/PyData/understanding-your-data-with-bayesian-networks-in-python-by-bartek-wilczynski | Understanding your data with Bayesian networks (in Python) by Bartek Wilczynski ]]

* [[https://dslpitt.org/genie/wiki/Python_Tutorials:_Tutorial_1:_Creating_a_Bayesian_Network | Python Tutorials: Tutorial 1: Creating a Bayesian Network]]

* [[ Bayesian Networks without Tears by Eugene Charniak | https://www.aaai.org/ojs/index.php/aimagazine/article/download/918/836]]

!!! Hidden Markov Models

!!! EM algorithm and Gaussian Mixture models

* Readings: Bishop, Chapter 9

*[[http://jellymatter.com/2013/10/01/intuitive-example-of-expectation-maximization/| Intuitive example of EM]]

*[[http://melodi.ee.washington.edu/people/bilmes/mypapers/em.pdf | A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models

to:

!!! Introduction to Kernel functions

* Readings: Bishop Chapter 6

!!! Support Vector Machines

* Readings: Bishop, Chapter 7

* [[http://users.ecs.soton.ac.uk/srg/publications/pdf/SVM.pdf | SVM for classification and regression]]

* [[http://pyml.sourceforge.net/doc/howto.pdf | A User’s Guide to Support Vector Machines ]]

* [[http://svmcompbio.tuebingen.mpg.de/plos-svm.pdf | Support Vector Machines and Kernels for Computational Biology]]

* Readings: Bishop Chapter 6

!!! Support Vector Machines

* Readings: Bishop, Chapter 7

* [[http://users.ecs.soton.ac.uk/srg/publications/pdf/SVM.pdf | SVM for classification and regression]]

* [[http://pyml.sourceforge.net/doc/howto.pdf | A User’s Guide to Support Vector Machines ]]

* [[http://svmcompbio.tuebingen.mpg.de/plos-svm.pdf | Support Vector Machines and Kernels for Computational Biology]]

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*

!!! Support Vector Machines

* Readings

* [[http:

* [[http://pyml

*

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!!! Bayesian networks: Representation & Inference

* Readings: Bishop, Chapter 8

* [[https://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html|A Brief Introduction to Graphical Models and Bayesian Networks. By Kevin Murphy]]

* [[https://www.autonlab.org/tutorials/bayesnet.html | Tutoral and Slides by Andrew Moore]]

* [[https://www.slideshare.net/PyData/understanding-your-data-with-bayesian-networks-in-python-by-bartek-wilczynski | Understanding your data with Bayesian networks (in Python) by Bartek Wilczynski ]]

* [[https://dslpitt.org/genie/wiki/Python_Tutorials:_Tutorial_1:_Creating_a_Bayesian_Network | Python Tutorials: Tutorial 1: Creating a Bayesian Network]]

* [[ Bayesian Networks without Tears by Eugene Charniak | https://www.aaai.org/ojs/index.php/aimagazine/article/download/918/836]]

!!! Hidden Markov Models

!!! EM algorithm and Gaussian Mixture models

* Readings: Bishop, Chapter 9

*[[http://jellymatter.com/2013/10/01/intuitive-example-of-expectation-maximization/| Intuitive example of EM]]

*[[http://melodi.ee.washington.edu/people/bilmes/mypapers/em.pdf | A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models ]]

* Readings: Bishop, Chapter 8

* [[https://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html|A Brief Introduction to Graphical Models and Bayesian Networks. By Kevin Murphy]]

* [[https://www.autonlab.org/tutorials/bayesnet.html | Tutoral and Slides by Andrew Moore]]

* [[https://www.slideshare.net/PyData/understanding-your-data-with-bayesian-networks-in-python-by-bartek-wilczynski | Understanding your data with Bayesian networks (in Python) by Bartek Wilczynski ]]

* [[https://dslpitt.org/genie/wiki/Python_Tutorials:_Tutorial_1:_Creating_a_Bayesian_Network | Python Tutorials: Tutorial 1: Creating a Bayesian Network]]

* [[ Bayesian Networks without Tears by Eugene Charniak | https://www.aaai.org/ojs/index.php/aimagazine/article/download/918/836]]

!!! Hidden Markov Models

!!! EM algorithm and Gaussian Mixture models

* Readings: Bishop, Chapter 9

*[[http://jellymatter.com/2013/10/01/intuitive-example-of-expectation-maximization/| Intuitive example of EM]]

*[[http://melodi.ee.washington.edu/people/bilmes/mypapers/em.pdf | A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models ]]

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I value student's opinions regarding the course and I will take them into consideration to make this course as exciting and engaging as possible. Thus, through the semester I will ask students formal and informal feedback. Formal feedback includes short surveys on my teaching effectiveness, preferred teaching methods, and the pace of the class. Informal feedback will be in the form of polls or in-class questions regarding learning preferences. You can also leave anonymous feedback in the form of a note in my departmental mailbox, under my office door~~ (FEC 325)~~, or %target=_blank%[[https://docs.google.com/spreadsheet/viewform?fromEmail=true&formkey=dHZvUElpWXJsTXV6RTAwZGlCTkYtQVE6MQ | using this form]]. Remember that it is in the best interest of the class if you bring up to my attention if something is not working properly (e.g the pace of the class is too slow, the projects are boring, my teaching style is not effective) so that I can make the corrective steps.

to:

I value student's opinions regarding the course and I will take them into consideration to make this course as exciting and engaging as possible. Thus, through the semester I will ask students formal and informal feedback. Formal feedback includes short surveys on my teaching effectiveness, preferred teaching methods, and the pace of the class. Informal feedback will be in the form of polls or in-class questions regarding learning preferences. You can also leave anonymous feedback in the form of a note in my departmental mailbox, under my office door, or %target=_blank%[[https://docs.google.com/spreadsheet/viewform?fromEmail=true&formkey=dHZvUElpWXJsTXV6RTAwZGlCTkYtQVE6MQ | using this form]]. Remember that it is in the best interest of the class if you bring up to my attention if something is not working properly (e.g the pace of the class is too slow, the projects are boring, my teaching style is not effective) so that I can make the corrective steps.

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Attendance to class is expected (read mandatory) and note taking encouraged. Important information (about exams, assignments, projects, policies) may be communicated only ~~in the lectures~~. We may also cover additional material (not available in the book~~) during the lecture. If you miss a ~~lecture~~,~~ you ~~should find what material was covered and if any announcement was~~ made.

to:

Attendance to class is expected (read mandatory) and note taking encouraged. Important information (about exams, assignments, projects, policies) may be communicated only during lecture time. We may also cover additional material (not available in the book or in slides) during the lecture.

If you miss a lecture, you should find what material was covered and if any announcement was made. If you have unexcused absences, this may result in participation points being deducted. Excused absences include sickness, attending conferences, job interviews, and similar. Even if your absence is excused, it is your responsibility to find out what material you missed. The professor is happy to answer specific questions regarding the lecture, but cannot go through all of the missed material on a one-to-one basis.

If you miss a lecture, you should find what material was covered and if any announcement was made. If you have unexcused absences, this may result in participation points being deducted. Excused absences include sickness, attending conferences, job interviews, and similar. Even if your absence is excused, it is your responsibility to find out what material you missed. The professor is happy to answer specific questions regarding the lecture, but cannot go through all of the missed material on a one-to-one basis.

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* Project 1, release date ~~Sep 5~~, '''due date Sep ~~25~~''' (~~decision trees~~)

* Project 2, release date Sep~~26~~, '''due date Oct ~~16~~''' (~~logistic regression)~~

* Project 3, release date Oct 24, ~~'''due~~ date ~~Nov 13~~'''~~ (cnns) ~~

* Project 4, release date Nov 14, ~~'''due~~ date ~~Dec 7~~'''~~ (kaggle competition or individual project~~)

* Project 2, release date Sep

* Project 3, release date Oct 24

* Project 4, release date Nov 14

to:

* Project 1, release date Aug 23, '''due date Sep 16''' (Decision Trees)

* Project 2, release date Sep 18, '''due date Oct 14''' (Naive Bayes and Logistic Regression)

* Project 3, release date Oct 16, '''due date Nov 11''' (LSTM/RNN)

* Project 4, release date Nov 13, '''due date Dec 7''' (kaggle competition)

* Project 2, release date Sep 18, '''due date Oct 14''' (Naive Bayes and Logistic Regression)

* Project 3, release date Oct 16, '''due date Nov 11''' (LSTM/RNN)

* Project 4, release date Nov 13, '''due date Dec 7''' (kaggle competition)

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Readings in this syllabus are mandatory, you can expect short individual quizzes every week. Those quizzes will be graded and they account for 10% of your final grade

to:

Readings in this syllabus are mandatory, you can expect short individual quizzes every week. Those quizzes will be graded and they account for 10% of your final grade. Quizzes are done first thing in the morning if you are late 5 minutes that may mean that you missed the quiz. If you have an excused absence and missed a quiz, you can make up for it by writing a 2-page summary of the reading. If your absence is unexcused, you cannot make up that quiz.

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Exams are our formal evaluation tool. In the exams you will be tested with respect to the learning goals of this course (see the schedule below for the list of learning goals). Exams will comprise a mix of practical exercises and concepts. I don't encourage you to learn concepts and definitions by heart, but to be able to explain them with your own words and to place these concepts into the broader context they belong to. There will be ~~one ~~'''~~midterm~~ exam ~~around~~ October ~~19~~''' ~~and one ~~'''~~final~~ exam ~~around Nov 16~~'''

'''The exam is open notes, but only personal, hand-written notes are accepted'''. Restrictions in this matter include (but are not limited): you cannot download notes from Internet, you cannot use the electronic notes of the course, and you cannot photocopy notes from your classmates. In fact, the key point is that they must be '''your own hand-written notes''' because I expect you to reinforce what you learned in class by writing down key concepts.

'''The exam is open notes, but only personal, hand-written notes are accepted'''. Restrictions in this matter include (but are not limited): you cannot download notes from Internet, you cannot use the electronic notes of the course, and you cannot photocopy notes from your classmates. In fact, the key point is that they must be '''your own hand-written notes''' because I expect you to reinforce what you learned in class by writing down key concepts.

to:

Exams are our formal evaluation tool. In the exams, you will be tested with respect to the learning goals of this course (see the schedule below for the list of learning goals). Exams will comprise a mix of practical exercises and concepts. I don't encourage you to learn concepts and definitions by heart, but to be able to explain them with your own words and to place these concepts into the broader context they belong to. There will be two exams:

* '''Midterm exam on October 9th'''

* '''Final exam on November 15th'''

'''The exam is open notes, but only personal, hand-written notes are accepted'''. Restrictions in this matter include (but are not limited): you cannot download notes from the Internet, you cannot use the electronic notes of the course, and you cannot photocopy notes from your classmates. In fact, the key point is that they must be '''your own hand-written notes''' because I expect you to reinforce what you learned in class by writing down key concepts.

* '''Midterm exam on October 9th'''

* '''Final exam on November 15th'''

'''The exam is open notes, but only personal, hand-written notes are accepted'''. Restrictions in this matter include (but are not limited): you cannot download notes from the Internet, you cannot use the electronic notes of the course, and you cannot photocopy notes from your classmates. In fact, the key point is that they must be '''your own hand-written notes''' because I expect you to reinforce what you learned in class by writing down key concepts.

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I value student's opinions regarding the course and I will take them ~~in~~ consideration to make this course as exciting and engaging as possible. Thus, through the semester I will ask students formal and informal feedback. Formal feedback includes short surveys on my teaching effectiveness, preferred teaching methods, and pace of the class. Informal feedback will be in the form of polls or in-class questions regarding learning preferences. You can also leave anonymous feedback in the form of a note in my departmental ~~mail box~~, under my office door (FEC 325), or %target=_blank%[[https://docs.google.com/spreadsheet/viewform?fromEmail=true&formkey=dHZvUElpWXJsTXV6RTAwZGlCTkYtQVE6MQ | using this form]]. Remember that it is in the best interest of the class if you bring up to my attention if something is not working properly (e.g the pace of the class is too slow, the projects are boring, my teaching style is not effective) so that I can make the corrective steps.

to:

I value student's opinions regarding the course and I will take them into consideration to make this course as exciting and engaging as possible. Thus, through the semester I will ask students formal and informal feedback. Formal feedback includes short surveys on my teaching effectiveness, preferred teaching methods, and the pace of the class. Informal feedback will be in the form of polls or in-class questions regarding learning preferences. You can also leave anonymous feedback in the form of a note in my departmental mailbox, under my office door (FEC 325), or %target=_blank%[[https://docs.google.com/spreadsheet/viewform?fromEmail=true&formkey=dHZvUElpWXJsTXV6RTAwZGlCTkYtQVE6MQ | using this form]]. Remember that it is in the best interest of the class if you bring up to my attention if something is not working properly (e.g the pace of the class is too slow, the projects are boring, my teaching style is not effective) so that I can make the corrective steps.

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Grades will be based on your earned points, following this ~~grade~~ scale. You need to get the specified number of points or more to obtain the grade from the same column. Scores will be rounded to the closest integer value.

to:

Grades will be based on your earned points, following this grading scale. You need to get the specified number of points or more to obtain the grade from the same column. Scores will be rounded to the closest integer value.

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* Readings: Bishop, Chapter 14

to:

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!!! Perceptrons & Neural Networks

* Readings: Mitchell, Chapter 4

* Readings: Bishop Chapter 5

* Super awesome set of videos [[https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU| Neural Networks Demystified]] (watch all 7 parts)

* [[http://lia.univ-avignon.fr/chercheurs/torres/livres/book-neuro-intro.pdf | An intoduction to Neural Networks, Ben Krose and Patrick van der Smagt]]

!!! Neural Architectures

* [[https://deeplearning4j.org/restrictedboltzmannmachine | Restricted Boltzman Machines]]

* [[https://www.oreilly.com/learning/generative-adversarial-networks-for-beginners \ GANs for beginners, Jon BrunerAdit Deshpande]]

* [[http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/ | GANs with tensorflow]]

* [[https://arxiv.org/pdf/1701.00160.pdf | Tutorial on GAns by Ian Goodfellow]]

!!! Deep Learning

* [[https://www.cs.toronto.edu/~hinton/science.pdf | Reducing the Dimensionality of Data with Neural Networks, Hinton and Salakhutdinov ]]

* [[https://www.youtube.com/watch?v=W15K9PegQt0 | Andrew Ng on Deep Learning]]

* [[http://deeplearning.net/demos/ | DL Demos]]

* [[https://tryolabs.com/blog/2016/12/06/major-advancements-deep-learning-2016/ | DL advances (2016)]]

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!!! Bias/Variance Tradeoff, Boosting and Bagging

* Readings: Bishop, Chapter 14

* Readings: Bishop, Chapter 14

Deleted lines 217-233:

* Readings: Mitchell, Chapter 4

* Readings: Bishop Chapter 5

* Super awesome set of videos [[https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU| Neural Networks Demystified]] (watch all 7 parts)

* [[http://lia.univ-avignon.fr/chercheurs/torres/livres/book-neuro-intro.pdf | An intoduction to Neural Networks, Ben Krose and Patrick van der Smagt]]

!!! Neural Architectures

* [[https://deeplearning4j.org/restrictedboltzmannmachine | Restricted Boltzman Machines]]

* [[https://www.oreilly.com/learning/generative-adversarial-networks-for-beginners \ GANs for beginners, Jon BrunerAdit Deshpande]]

* [[http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/ | GANs with tensorflow]]

* [[https://arxiv.org/pdf/1701.00160.pdf | Tutorial on GAns by Ian Goodfellow]]

!!! Deep Learning

* [[https://www.cs.toronto.edu/~hinton/science.pdf | Reducing the Dimensionality of Data with Neural Networks, Hinton and Salakhutdinov ]]

* [[https://www.youtube.com/watch?v=W15K9PegQt0 | Andrew Ng on Deep Learning]]

* [[http://deeplearning.net/demos/ | DL Demos]]

* [[https://tryolabs.com/blog/2016/12/06/major-advancements-deep-learning-2016/ | DL advances (2016)]]

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* [[https://www.youtube.com/watch?v=F1ka6a13S9I | Andrew Ng, Nuts and bolts]]

to:

* [[https://www.youtube.com/watch?v=F1ka6a13S9I | Andrew Ng, Nuts and bolts for DL]]

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!! Best practices in ML

* [[https://www.youtube.com/watch?v=NKiwFF_zBu4 | Ian Goodfellow, Practical Methodology for Deploying Machine Learning ]]

* [[https://www.youtube.com/playlist?list=PLnnr1O8OWc6Y5PZIqgzArEH1oFlwk0PFq | Andrew Ng, Advice for applying ML]]

* [[https://www.youtube.com/watch?v=F1ka6a13S9I | Andrew Ng, Nuts and bolts]]

Changed lines 164-182 from:

* Readings: Mitchell, Chapter 4

* Readings: Bishop Chapter 5

* Super awesome set of videos [[https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU| Neural Networks Demystified]] (watch all 7 parts)

* [[http://lia.univ-avignon.fr/chercheurs/torres/livres/book-neuro-intro.pdf | An intoduction to Neural Networks, Ben Krose and Patrick van der Smagt]]

!!! Neural Architectures

* [[https://deeplearning4j.org/restrictedboltzmannmachine | Restricted Boltzman Machines]]

* [[https://www.oreilly.com/learning/generative-adversarial-networks-for-beginners \ GANs for beginners, Jon BrunerAdit Deshpande]]

* [[http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/ | GANs with tensorflow]]

* [[https://arxiv.org/pdf/1701.00160.pdf | Tutorial on GAns by Ian Goodfellow]]

!!! Deep Learning

* [[https://www.cs.toronto.edu/~hinton/science.pdf | Reducing the Dimensionality of Data with Neural Networks, Hinton and Salakhutdinov ]]

* [[https://www.youtube.com/watch?v=W15K9PegQt0 | Andrew Ng on Deep Learning]]

* [[http://deeplearning.net/demos/ | DL Demos]]

* [[https://tryolabs.com/blog/2016/12/06/major-advancements-deep-learning-2016/ | DL advances (2016)]]

to:

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* Readings: Mitchell, Chapter ~~7~~

to:

* Readings: Mitchell, Chapter 7

!!! Perceptrons & Neural Networks

* Readings: Mitchell, Chapter 4

* Readings: Bishop Chapter 5

* Super awesome set of videos [[https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU| Neural Networks Demystified]] (watch all 7 parts)

* [[http://lia.univ-avignon.fr/chercheurs/torres/livres/book-neuro-intro.pdf | An intoduction to Neural Networks, Ben Krose and Patrick van der Smagt]]

!!! Neural Architectures

* [[https://deeplearning4j.org/restrictedboltzmannmachine | Restricted Boltzman Machines]]

* [[https://www.oreilly.com/learning/generative-adversarial-networks-for-beginners \ GANs for beginners, Jon BrunerAdit Deshpande]]

* [[http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/ | GANs with tensorflow]]

* [[https://arxiv.org/pdf/1701.00160.pdf | Tutorial on GAns by Ian Goodfellow]]

!!! Deep Learning

* [[https://www.cs.toronto.edu/~hinton/science.pdf | Reducing the Dimensionality of Data with Neural Networks, Hinton and Salakhutdinov ]]

* [[https://www.youtube.com/watch?v=W15K9PegQt0 | Andrew Ng on Deep Learning]]

* [[http://deeplearning.net/demos/ | DL Demos]]

* [[https://tryolabs.com/blog/2016/12/06/major-advancements-deep-learning-2016/ | DL advances (2016)]]

!!! Perceptrons & Neural Networks

* Readings: Mitchell, Chapter 4

* Readings: Bishop Chapter 5

* Super awesome set of videos [[https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU| Neural Networks Demystified]] (watch all 7 parts)

* [[http://lia.univ-avignon.fr/chercheurs/torres/livres/book-neuro-intro.pdf | An intoduction to Neural Networks, Ben Krose and Patrick van der Smagt]]

!!! Neural Architectures

* [[https://deeplearning4j.org/restrictedboltzmannmachine | Restricted Boltzman Machines]]

* [[https://www.oreilly.com/learning/generative-adversarial-networks-for-beginners \ GANs for beginners, Jon BrunerAdit Deshpande]]

* [[http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/ | GANs with tensorflow]]

* [[https://arxiv.org/pdf/1701.00160.pdf | Tutorial on GAns by Ian Goodfellow]]

!!! Deep Learning

* [[https://www.cs.toronto.edu/~hinton/science.pdf | Reducing the Dimensionality of Data with Neural Networks, Hinton and Salakhutdinov ]]

* [[https://www.youtube.com/watch?v=W15K9PegQt0 | Andrew Ng on Deep Learning]]

* [[http://deeplearning.net/demos/ | DL Demos]]

* [[https://tryolabs.com/blog/2016/12/06/major-advancements-deep-learning-2016/ | DL advances (2016)]]

Changed lines 177-178 from:

* [[https://www.cs.toronto.edu/~hinton/science.pdf | Reducing the Dimensionality of

~~Data~~ with Neural Networks, Hinton and Salakhutdinov]]

to:

* [[https://www.cs.toronto.edu/~hinton/science.pdf | Reducing the Dimensionality of Data with Neural Networks, Hinton and Salakhutdinov ]]

Changed lines 174-175 from:

to:

* [[https://arxiv.org/pdf/1701.00160.pdf | Tutorial on GAns by Ian Goodfellow]]

Added lines 177-178:

* [[https://www.cs.toronto.edu/~hinton/science.pdf | Reducing the Dimensionality of

Data with Neural Networks, Hinton and Salakhutdinov]]

Data with Neural Networks, Hinton and Salakhutdinov]]

Added lines 172-173:

* [[https://www.oreilly.com/learning/generative-adversarial-networks-for-beginners \ GANs for beginners, Jon BrunerAdit Deshpande]]

* [[http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/ | GANs with tensorflow]]

* [[http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/ | GANs with tensorflow]]

Added lines 169-171:

!!! Neural Architectures

* [[https://deeplearning4j.org/restrictedboltzmannmachine | Restricted Boltzman Machines]]

Added line 173:

* [[https://tryolabs.com/blog/2016/12/06/major-advancements-deep-learning-2016/ | DL advances (2016)]]

Added line 189:

* [[http://users.ecs.soton.ac.uk/srg/publications/pdf/SVM.pdf | SVM for classification and regression]]

Added line 153:

* [[ Bayesian Networks without Tears by Eugene Charniak | https://www.aaai.org/ojs/index.php/aimagazine/article/download/918/836]]

Changed lines 138-141 from:

* Readings: Bishop, Chapter 8

to:

Changed lines 147-148 from:

!!! Bayesian ~~Networks~~

to:

!!! Bayesian networks: Representation & Inference

* Readings: Bishop, Chapter 8

* Readings: Bishop, Chapter 8

Added lines 150-164:

!!! Bayesian Networks

* [[https://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html|A Brief Introduction to Graphical Models and Bayesian Networks. By Kevin Murphy]]

* [[https://www.autonlab.org/tutorials/bayesnet.html | Tutoral and Slides by Andrew Moore]]

* [[https://www.slideshare.net/PyData/understanding-your-data-with-bayesian-networks-in-python-by-bartek-wilczynski | Understanding your data with Bayesian networks (in Python) by Bartek Wilczynski ]]

* [[https://dslpitt.org/genie/wiki/Python_Tutorials:_Tutorial_1:_Creating_a_Bayesian_Network | Python Tutorials: Tutorial 1: Creating a Bayesian Network]]

!!! Hidden Markov Models

!!! EM algorithm and Gaussian Mixture models

* Readings: Bishop, Chapter 9

*[[http://jellymatter.com/2013/10/01/intuitive-example-of-expectation-maximization/| Intuitive example of EM]]

*[[http://melodi.ee.washington.edu/people/bilmes/mypapers/em.pdf | A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models ]]

* [[https://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html|A Brief Introduction to Graphical Models and Bayesian Networks. By Kevin Murphy]]

* [[https://www.autonlab.org/tutorials/bayesnet.html | Tutoral and Slides by Andrew Moore]]

* [[https://www.slideshare.net/PyData/understanding-your-data-with-bayesian-networks-in-python-by-bartek-wilczynski | Understanding your data with Bayesian networks (in Python) by Bartek Wilczynski ]]

* [[https://dslpitt.org/genie/wiki/Python_Tutorials:_Tutorial_1:_Creating_a_Bayesian_Network | Python Tutorials: Tutorial 1: Creating a Bayesian Network]]

!!! Hidden Markov Models

!!! EM algorithm and Gaussian Mixture models

* Readings: Bishop, Chapter 9

*[[http://jellymatter.com/2013/10/01/intuitive-example-of-expectation-maximization/| Intuitive example of EM]]

*[[http://melodi.ee.washington.edu/people/bilmes/mypapers/em.pdf | A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models ]]

Deleted lines 191-196:

!!! EM algorithm and Gaussian Mixture models

* Readings: Bishop, Chapter 9

*[[http://jellymatter.com/2013/10/01/intuitive-example-of-expectation-maximization/| Intuitive example of EM]]

*[[http://melodi.ee.washington.edu/people/bilmes/mypapers/em.pdf | A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models ]]

Changed lines 62-67 from:

Homework consists of a set of theoretical questions. Homework is individual. Only students enrolled in the '''429'''(undergraduate) section can work in groups of 2

Academic honesty will be thoroughly enforced for both projects and homework. If a violation is found, ALL the students involved will get a 0. If a violation is repeated, the student will get an F in the course.

to:

Changed lines 92-94 from:

* Participation ~~10~~ pts

*~~Homework~~ ~~10 pts~~

* Quizzes 10 pts

*

* Quizzes 10

to:

* Participation 15 pts

* Quizzes 15 pts

* Quizzes 15 pts

Added lines 138-141:

!!! Bayesian networks: Representation & Inference

* Readings: Bishop, Chapter 8

* Readings: Bishop, Chapter 8

Added lines 147-149:

!!! Bias/Variance Tradeoff, Boosting and Bagging

* Readings: Bishop, Chapter 14

* Readings: Bishop, Chapter 14

Deleted lines 184-192:

* Readings: Bishop, Chapter 8

!!! Bayesian networks: Inference

* Readings: Bishop, Chapter 8

!!! Hidden Markov models

* Readings: Bishop, Chapter 13

Deleted lines 187-188:

* Readings: Bishop, Chapter 14

Changed lines 48-58 from:

Projects and homework reinforce what you learned in class by materializing abstract concepts into practical problems. Every project usually consists of a programming assignment. ~~Schedule~~ is as follows:

* Project 1, release date~~Jan 20~~, '''due date ~~Feb 3~~''' (decision trees)

* Project 2, release date~~Feb~~ 26, '''due date ~~Mar 12~~''' (~~naive bayes~~)

* Project 3, release date~~Mar 12~~, '''due date ~~Apr 2~~''' (~~logistic regression~~)

* Project 4, release date~~Apr 2~~, '''due date ~~Apr 23~~''' (kaggle competition~~)~~

You can discuss projects with other classmates but all the code have to be written by you. Any student suspected of plagiarizing code will be prosecuted according to the University guidelines. Note that on '''3/3/15''' students received a formal warning that any cheating incident will result in a '''failing grade'''.

* Projects have to be uploaded to UNM Learn and late projects will be accepted only within the following policy:

* Project 1, release date

* Project 2, release date

* Project 3, release date

* Project 4, release date

You can discuss projects with other classmates but all the code have to be written by you. Any student suspected of plagiarizing code will be prosecuted according to the University guidelines. Note that on '''3/3/15''' students received a formal warning that any cheating incident will result in a '''failing grade'''.

* Projects have to be uploaded to UNM Learn and late

to:

Projects and homework reinforce what you learned in class by materializing abstract concepts into practical problems. Every project usually consists of a programming assignment. Tentative schedule is as follows:

* Project 1, release date Sep 5, '''due date Sep 25''' (decision trees)

* Project 2, release date Sep 26, '''due date Oct 16''' (logistic regression)

* Project 3, release date Oct 24, '''due date Nov 13''' (cnns)

* Project 4, release date Nov 14, '''due date Dec 7''' (kaggle competition or individual project)

You can discuss projects with other classmates but all the code have to be written by you. Any student suspected of plagiarizing code will be prosecuted according to the University guidelines.

* Project reports have to be uploaded to UNM Learn, code and solution have to be uploaded through Kaggle. Late projects will be accepted only within the following policy:

* Project 1, release date Sep 5, '''due date Sep 25''' (decision trees)

* Project 2, release date Sep 26, '''due date Oct 16''' (logistic regression)

* Project 3, release date Oct 24, '''due date Nov 13''' (cnns)

* Project 4, release date Nov 14, '''due date Dec 7''' (kaggle competition or individual project)

You can discuss projects with other classmates but all the code have to be written by you. Any student suspected of plagiarizing code will be prosecuted according to the University guidelines.

* Project reports have to be uploaded to UNM Learn, code and solution have to be uploaded through Kaggle. Late projects will be accepted only within the following policy:

Changed lines 64-65 from:

Homework consists of a set of theoretical questions. Homework ~~are~~ individual. Only students enrolled in the '''429'''(undergraduate) section can work in groups of 2

to:

Homework consists of a set of theoretical questions. Homework is individual. Only students enrolled in the '''429'''(undergraduate) section can work in groups of 2

Changed lines 81-82 from:

Exams are our formal evaluation tool. In the exams you will be tested with respect to the learning goals of this course (see the schedule below for the list of learning goals). Exams will comprise a mix of practical exercises and concepts. I don't encourage you to learn concepts and definitions by ~~hart~~, but to be able to explain them with your own words and to place these concepts into the broader context they belong to. There will be one '''midterm exam ~~on April 7th~~''' and one '''final exam ~~on April 28th~~'''

to:

Exams are our formal evaluation tool. In the exams you will be tested with respect to the learning goals of this course (see the schedule below for the list of learning goals). Exams will comprise a mix of practical exercises and concepts. I don't encourage you to learn concepts and definitions by heart, but to be able to explain them with your own words and to place these concepts into the broader context they belong to. There will be one '''midterm exam around October 19''' and one '''final exam around Nov 16'''

Changed line 107 from:

95 90 ~~87 83~~ 80 77 73 70 67 63 60 <60

to:

97 95 90 85 80 77 73 70 67 63 60 <60

Added lines 12-13:

* '''Deep Learning'''. Ian Goodfellow, Yoshua Begio, Aaron Courville. ISBN-10: 0262035618 | ISBN-13: 978-0262035613 (optional)

Deleted lines 109-111:

!!! Title IX:

In an effort to meet obligations under Title IX, UNM faculty, Teaching Assistants, and Graduate Assistants are considered “responsible employees” by the Department of Education (see pg 15 - http://www2.ed.gov/about/offices/list/ocr/docs/qa-201404-title-ix.pdf). This designation requires that any report of gender discrimination which includes sexual harassment, sexual misconduct and sexual violence made to a faculty member, TA, or GA must be reported to the Title IX Coordinator at the Office of Equal Opportunity (oeo.unm.edu). For more information on the campus policy regarding sexual misconduct, see: https://policy.unm.edu/university-policies/2000/2740.html

Changed lines 111-113 from:

!! ~~SPECIAL ACCOMMODATIONS~~

If you need special accommodations or assistance, please contact the Accessibility Resource Center(http://~~as2~~.~~unm~~.~~edu~~/)

If you need special accommodations or assistance, please contact the Accessibility Resource Center

to:

!!! Title IX:

In an effort to meet obligations under Title IX, UNM faculty, Teaching Assistants, and Graduate Assistants are considered “responsible employees” by the Department of Education (see pg 15 - http://www2.ed.gov/about/offices/list/ocr/docs/qa-201404-title-ix.pdf). This designation requires that any report of gender discrimination which includes sexual harassment, sexual misconduct and sexual violence made to a faculty member, TA, or GA must be reported to the Title IX Coordinator at the Office of Equal Opportunity (oeo.unm.edu). For more information on the campus policy regarding sexual misconduct, see: https://policy.unm.edu/university-policies/2000/2740.html

!! ADA:

In accordance with University Policy 2310 and the Americans with Disabilities Act (ADA), academic accommodations may be made for any student who notifies the instructor of the need for an accommodation. If you have a disability, either permanent or temporary, contact Accessibility Resource Center at 277-3506 for additional information.

In an effort to meet obligations under Title IX, UNM faculty, Teaching Assistants, and Graduate Assistants are considered “responsible employees” by the Department of Education (see pg 15 - http://www2.ed.gov/about/offices/list/ocr/docs/qa-201404-title-ix.pdf). This designation requires that any report of gender discrimination which includes sexual harassment, sexual misconduct and sexual violence made to a faculty member, TA, or GA must be reported to the Title IX Coordinator at the Office of Equal Opportunity (oeo.unm.edu). For more information on the campus policy regarding sexual misconduct, see: https://policy.unm.edu/university-policies/2000/2740.html

!! ADA:

In accordance with University Policy 2310 and the Americans with Disabilities Act (ADA), academic accommodations may be made for any student who notifies the instructor of the need for an accommodation. If you have a disability, either permanent or temporary, contact Accessibility Resource Center at 277-3506 for additional information.

Added lines 152-156:

!!! Deep Learning

* [[https://www.youtube.com/watch?v=W15K9PegQt0 | Andrew Ng on Deep Learning]]

* [[http://deeplearning.net/demos/ | DL Demos]]

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!!! Data representation LSI, ICA, ~~Deep belief networks~~

to:

!!! Data representation LSI, ICA, Autoencoders

Changed lines 153-159 from:

!!! ~~Introduction to Kernel functions~~

* Readings: Bishop Chapter 6

!!! Support Vector Machines

* Readings: Bishop, Chapter 7

!!! Principal Component Analysis

* Readings: Bishop Chapter 6

!!! Support Vector Machines

* Readings: Bishop, Chapter 7

!!! Principal Component Analysis

to:

!!! Dimensionality reduction, PCA, SVD

Added lines 157-169:

!!! Data representation LSI, ICA, Deep belief networks

*[[https://www.cs.toronto.edu/~hinton/science.pdf]]

*[[http://www.cs.toronto.edu/~fritz/absps/dbm.pdf]]

!!! Introduction to Kernel functions

* Readings: Bishop Chapter 6

!!! Support Vector Machines

* Readings: Bishop, Chapter 7

* [[http://pyml.sourceforge.net/doc/howto.pdf | A User’s Guide to Support Vector Machines ]]

* [[http://svmcompbio.tuebingen.mpg.de/plos-svm.pdf | Support Vector Machines and Kernels for Computational Biology]]

Added line 161:

*[[http://www.visiondummy.com/2014/05/feature-extraction-using-pca/ | Vincent Spruyt's tutorial]]

Added lines 159-162:

!!! Principal Component Analysis

* Readings: Bishop Chapter 12

*[[http://blog.cordiner.net/2010/12/02/eigenfaces-face-recognition-matlab/ | Eigenfaces tutorial]]

* Readings: Bishop Chapter 12

*[[http://blog.cordiner.net/2010/12/02/eigenfaces-face-recognition-matlab/ | Eigenfaces tutorial]]

Added lines 167-168:

Deleted lines 176-180:

!!! Principal Component Analysis

* Readings: Bishop Chapter 12

*[[http://blog.cordiner.net/2010/12/02/eigenfaces-face-recognition-matlab/ | Eigenfaces tutorial]]

Added lines 153-162:

!!! Introduction to Kernel functions

* Readings: Bishop Chapter 6

!!! Support Vector Machines

* Readings: Bishop, Chapter 7

!!! EM algorithm and Gaussian Mixture models

* Readings: Bishop, Chapter 9

*[[http://jellymatter.com/2013/10/01/intuitive-example-of-expectation-maximization/| Intuitive example of EM]]

*[[http://melodi.ee.washington.edu/people/bilmes/mypapers/em.pdf | A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models ]]

* Readings: Bishop Chapter 6

!!! Support Vector Machines

* Readings: Bishop, Chapter 7

!!! EM algorithm and Gaussian Mixture models

* Readings: Bishop, Chapter 9

*[[http://jellymatter.com/2013/10/01/intuitive-example-of-expectation-maximization/| Intuitive example of EM]]

*[[http://melodi.ee.washington.edu/people/bilmes/mypapers/em.pdf | A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models ]]

Deleted lines 168-172:

* Readings: Bishop, Chapter 9

*[[http://jellymatter.com/2013/10/01/intuitive-example-of-expectation-maximization/| Intuitive example of EM]]

*[[http://melodi.ee.washington.edu/people/bilmes/mypapers/em.pdf | A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models ]]

Deleted lines 175-179:

* Readings: Bishop Chapter 6

!!! Support Vector Machines

* Readings: Bishop, Chapter 7

Added line 151:

* [[http://lia.univ-avignon.fr/chercheurs/torres/livres/book-neuro-intro.pdf | An intoduction to Neural Networks, Ben Krose and Patrick van der Smagt]]

Added line 150:

* Super awesome set of videos [[https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU| Neural Networks Demystified]] (watch all 7 parts)

Changed line 125 from:

* [[http://~~www~~.~~dmi.unict.it~~/~~~apulvirenti~~/~~agd/Qui86~~.pdf | Quinlan's Induction of Decision Trees]]

to:

* [[http://hunch.net/~coms-4771/quinlan.pdf | Quinlan's Induction of Decision Trees]]

Changed line 131 from:

* [[~~Paola Sebastiani's tutorial | ~~http://www.sci.utah.edu/~gerig/CS6640-F2010/prob-tut.pdf]]

to:

* [[http://www.sci.utah.edu/~gerig/CS6640-F2010/prob-tut.pdf | Paola Sebastiani's tutorial]]

Added lines 25-36:

Any and all acts of plagiarism will result in an immediate dismissal from the course and an official report to the dean of students.

Instances of plagiarism include, but are not limited to: downloading code and snippets from the Internet without explicit permission from the instructor and/or without proper acknowledgment, citation, or license use; using code from a classmate or any other past or present student; quoting text directly or slightly paraphrasing from a source without proper reference; any other act of copying material and trying to make it look like it is yours.

Note that dismissal from the class means that the student will be dropped with an F from the course.

The best way of avoiding plagiarism is to start your assignments early. Whenever you feel like you cannot keep up with the course material, your instructor is happy to find a way to help you. Make an appointment or come to office hours, but DO NOT plagiarize; it is not worth it!

Change of grade to CR/NC after the semester deadline will be granted ONLY under special, documented extenuating circumstances.

Instances of plagiarism include, but are not limited to: downloading code and snippets from the Internet without explicit permission from the instructor and/or without proper acknowledgment, citation, or license use; using code from a classmate or any other past or present student; quoting text directly or slightly paraphrasing from a source without proper reference; any other act of copying material and trying to make it look like it is yours.

Note that dismissal from the class means that the student will be dropped with an F from the course.

The best way of avoiding plagiarism is to start your assignments early. Whenever you feel like you cannot keep up with the course material, your instructor is happy to find a way to help you. Make an appointment or come to office hours, but DO NOT plagiarize; it is not worth it!

Change of grade to CR/NC after the semester deadline will be granted ONLY under special, documented extenuating circumstances.

Changed lines 51-53 from:

* Project 4, release date Apr 2, '''due date Apr 23''' (~~neural networks, svm, ensembles~~)

to:

* Project 4, release date Apr 2, '''due date Apr 23''' (kaggle competition)

Changed lines 66-72 from:

!!! ~~Daily assignments and ~~quizzes

~~You can expect to have simple exercises and quizzes every meeting. These daily assignments will be done in groups specified by the instructor and they will account for your participation ~~grade ~~(15%~~ of your final grade)

to:

!!! Weekly quizzes

Readings in this syllabus are mandatory, you can expect short individual quizzes every week. Those quizzes will be graded and they account for 10% of your final grade

!!! Daily assignments

You can expect to have simple exercises every meeting. These daily assignments will be done in groups specified by the instructor and they will account for your participation grade (10% of your final grade)

Readings in this syllabus are mandatory, you can expect short individual quizzes every week. Those quizzes will be graded and they account for 10% of your final grade

!!! Daily assignments

You can expect to have simple exercises every meeting. These daily assignments will be done in groups specified by the instructor and they will account for your participation grade (10% of your final grade)

Changed lines 95-96 from:

* Participation ~~15~~ pts

* Homework~~20~~ pts

* Homework

to:

* Participation 10 pts

* Homework 10 pts

* Quizzes 10 pts

* Homework 10 pts

* Quizzes 10 pts

Changed lines 99-102 from:

* ~~Paper ~~ ~~ 5 pts~~

* Exams 20 pts

* Exams 20

to:

* Exams 30 pts

Changed line 123 from:

!!! ~~Decision Trees~~

to:

!!! CART

Added line 131:

* [[Paola Sebastiani's tutorial | http://www.sci.utah.edu/~gerig/CS6640-F2010/prob-tut.pdf]]

Changed line 42 from:

You can discuss projects with other classmates but all the code have to be written by you. Any student suspected of plagiarizing code will be prosecuted according to the University guidelines. Note that on '''~~2~~/~~28~~/15''' students received a formal warning that any cheating incident will result in a '''failing grade'''.

to:

You can discuss projects with other classmates but all the code have to be written by you. Any student suspected of plagiarizing code will be prosecuted according to the University guidelines. Note that on '''3/3/15''' students received a formal warning that any cheating incident will result in a '''failing grade'''.

Changed line 42 from:

You can discuss projects with other classmates but all the code have to be written by you.

to:

You can discuss projects with other classmates but all the code have to be written by you. Any student suspected of plagiarizing code will be prosecuted according to the University guidelines. Note that on '''2/28/15''' students received a formal warning that any cheating incident will result in a '''failing grade'''.

Changed line 63 from:

Exams are our formal evaluation tool. In the exams you will be tested with respect to the learning goals of this course (see the schedule below for the list of learning goals). Exams will comprise a mix of practical exercises and concepts. I don't encourage you to learn concepts and definitions by hart, but to be able to explain them with your own words and to place these concepts into the broader context they belong to. There will be one '''midterm exam on April 7th''' and one '''final exam on ~~May 5th~~'''

to:

Exams are our formal evaluation tool. In the exams you will be tested with respect to the learning goals of this course (see the schedule below for the list of learning goals). Exams will comprise a mix of practical exercises and concepts. I don't encourage you to learn concepts and definitions by hart, but to be able to explain them with your own words and to place these concepts into the broader context they belong to. There will be one '''midterm exam on April 7th''' and one '''final exam on April 28th'''

Added lines 130-133:

!!! Perceptrons & Neural Networks

* Readings: Mitchell, Chapter 4

* Readings: Bishop Chapter 5

* Readings: Mitchell, Chapter 4

* Readings: Bishop Chapter 5

Deleted lines 146-149:

!!! Perceptrons & Neural Networks

* Readings: Mitchell, Chapter 4

* Readings: Bishop Chapter 5

Changed line 45 from:

** Every student has ~~6~~ free days to be used at their own discretion across the multiple projects

to:

** Every student has 4 free days to be used at their own discretion across the multiple projects

Changed line 63 from:

Exams are our formal evaluation tool. In the exams you will be tested with respect to the learning goals of this course (see the schedule below for the list of learning goals). Exams will comprise a mix of practical exercises and concepts. I don't encourage you to learn concepts and definitions by hart, but to be able to explain them with your own words and to place these concepts into the broader context they belong to. There will be ~~only ~~one '''midterm exam on April ~~8th~~'''

to:

Exams are our formal evaluation tool. In the exams you will be tested with respect to the learning goals of this course (see the schedule below for the list of learning goals). Exams will comprise a mix of practical exercises and concepts. I don't encourage you to learn concepts and definitions by hart, but to be able to explain them with your own words and to place these concepts into the broader context they belong to. There will be one '''midterm exam on April 7th''' and one '''final exam on May 5th'''

Changed lines 32-35 from:

!!! Projects and ~~Homeworks: ~~

Projects and homework reinforce what you learned in class by materializing abstract concepts into practical problems. Every homework usually consists of a programming assignment. Schedule is as follows:

Projects and homework reinforce what you learned in class by materializing abstract concepts into practical problems. Every homework

to:

!!! Projects:

Projects and homework reinforce what you learned in class by materializing abstract concepts into practical problems. Every project usually consists of a programming assignment. Schedule is as follows:

Projects and homework reinforce what you learned in class by materializing abstract concepts into practical problems. Every project usually consists of a programming assignment. Schedule is as follows:

Changed lines 42-52 from:

You can discuss ~~homework~~ with other classmates but all the code have to be written by you. ~~When significant portions of the homework are done in groups, you are expected to acknowledge all~~ the ~~other students that participated in the discussion and outline what you learned specifically from it. ~~

* Homework has to be uploaded to UNM Learn and late homework will be accepted only within the following policy:

** Every student has 6 free days to be used at their own discretion across the multiple homeworks

** Once a student has used all of ~~his/her free days, no other late homework will be accepted ~~

* Students enrolled in the '''429''' section can work in groups of 3

* Students enrolled in the '''529''' section can work in groups of 2

* Homework has to be uploaded to UNM Learn and late homework will be accepted only within the following policy:

** Every student has 6 free days to be used at their own discretion across the multiple homeworks

** Once a student has used all

* Students enrolled in the '''429''' section can work in groups of 3

* Students enrolled in the '''529''' section can work in groups of 2

to:

You can discuss projects with other classmates but all the code have to be written by you.

* Projects have to be uploaded to UNM Learn and late projects will be accepted only within the following policy:

** Every student has 6 free days to be used at their own discretion across the multiple projects

** Once a student has used all of his/her free days, no other late project will be accepted

!!! Homework

Homework consists of a set of theoretical questions. Homework are individual. Only students enrolled in the '''429'''(undergraduate) section can work in groups of 2

Academic honesty will be thoroughly enforced for both projects and homework. If a violation is found, ALL the students involved will get a 0. If a violation is repeated, the student will get an F in the course.

* Projects have to be uploaded to UNM Learn and late projects will be accepted only within the following policy:

** Every student has 6 free days to be used at their own discretion across the multiple projects

** Once a student has used all of his/her free days, no other late project will be accepted

!!! Homework

Homework consists of a set of theoretical questions. Homework are individual. Only students enrolled in the '''429'''(undergraduate) section can work in groups of 2

Academic honesty will be thoroughly enforced for both projects and homework. If a violation is found, ALL the students involved will get a 0. If a violation is repeated, the student will get an F in the course.

Changed lines 80-81 from:

* Homework ~~30~~ pts

* Projects~~35~~ pts

* Projects

to:

* Homework 20 pts

* Projects 40 pts

* Paper 5 pts

* Projects 40 pts

* Paper 5 pts

Added line 85:

Deleted lines 105-110:

!!! Inductive Learning

* Readings: Mitchell, Chapter 1

!!! Concept Learning

* Readings: Mitchell, Chapter 2

Changed lines 35-41 from:

* ~~Homework~~ 1, release date Jan ~~13~~, '''due date ~~Jan 20''' (introduction) ~~

* Homework 2, release date -, '''due date -''' (probability)

* Homework 3, release date -, '''due date -''' ()

* Homework 4, release date -, '''due date -''' ()

* Homework 5, release date -, '''due date -''' ()

* Project 1, release date Jan 22, '''due date Feb 5''' (decision trees)

* Homework 2, release date -, '''due date -''' (probability)

* Homework 3, release date -, '''due date -''' ()

* Homework 4, release date -, '''due date -''' ()

* Homework 5, release date -, '''due date -''' ()

* Project 1, release date Jan 22, '''due date Feb 5

to:

* Project 1, release date Jan 20, '''due date Feb 3''' (decision trees)

Changed lines 57-70 from:

Projects are one of the most important learning tools of this class. The final project is entirely to the discretion of the student (upon instructor approval). Students would be free to explore a problem of their interest and propose their own solution. You must turn in only code written by you. Under no circumstance you should use code downloaded from Internet since this violation will result in serious penalties.

* Students enrolled in the '''429''' section can work in groups of 2

* Students enrolled in the '''529''' section must work individually

The project has the following deliverables:

* Proposal: maximum 1 page of project proposal, why the problem is important, what has been done so far in the field, and what are the expected outcomes

** '''Due date: Feb 27'''

* Poster and report

** '''Due date: May 5'''

to:

Changed lines 85-87 from:

* Homework ~~35~~ pts

*~~Project~~ 35 pts

*~~Exam~~ ~~15~~ pts

*

*

to:

* Homework 30 pts

* Projects 35 pts

* Exams 20 pts

* Projects 35 pts

* Exams 20 pts

Changed lines 35-40 from:

* Homework 1, release date Jan 13, '''due date Jan 20''' (~~probability~~)

to:

* Homework 1, release date Jan 13, '''due date Jan 20''' (introduction)

* Homework 2, release date -, '''due date -''' (probability)

* Homework 3, release date -, '''due date -''' ()

* Homework 4, release date -, '''due date -''' ()

* Homework 5, release date -, '''due date -''' ()

* Homework 2, release date -, '''due date -''' (probability)

* Homework 3, release date -, '''due date -''' ()

* Homework 4, release date -, '''due date -''' ()

* Homework 5, release date -, '''due date -''' ()

Changed lines 3-31 from:

* Class Time: '''TR 9:30-10:45 AM'''

* Building and Room: '''Dane Smith Hall 225'''

* Prerequisites: '''362 and STAT 345 and (MATH 314 or MATH 321)'''

* Piazza link: '''piazza.com/unm/spring2014/cs429529'''

!!! Instructor

* '''Trilce Estrada''', Assistant Professor

* Email: '''estrada@cs.unm.edu'''

* Office: ''' FEC 325 '''

* Office hours: '''Tuesday 11:00-1:00''' and '''Thursday 11:00-12:00'''

!!! Teaching Assistants

Name: '''Qi Lu'''

* Email: '''lukey11@unm.edu'''

* Office: '''FEC 116'''

* Office hours: '''Wed 1:00-3:00'''

Name: '''Dejun Jiang'''

* Email: '''pwinter@unm.edu'''

* Office: '''FEC 126'''

* Office hours: '''Wed 10:00-12:00'''

to:

Changed lines 32-39 from:

!!! Homeworks:

~~Homeworks reinforce what you learned in class by materializing abstract concepts into practical~~ problems. Every homework usually consists of a programming assignment. ~~Homeworks schedule ~~is as follows:

* Homework 1, release date Jan~~30~~, '''due date ~~Feb 13~~'''

~~* Homework 2,~~ release date ~~Feb 27~~, '''due date ~~Mar 13~~'''

~~* Homework 3,~~ release date ~~Mar 13~~, '''due date ~~Apr 3~~'''

~~* Homework 4,~~ release date ~~Apr 3~~, '''due date Apr ~~17~~'''

~~* Homework 5, release date Apr 17, '''due date May 1~~'''

* Homework 1, release date Jan

to:

!!! Projects and Homeworks:

Projects and homework reinforce what you learned in class by materializing abstract concepts into practical problems. Every homework usually consists of a programming assignment. Schedule is as follows:

* Homework 1, release date Jan 13, '''due date Jan 20''' (probability)

* Project 1, release date Jan 22, '''due date Feb 5''' (decision trees)

* Project 2, release date Feb 26, '''due date Mar 12''' (naive bayes)

* Project 3, release date Mar 12, '''due date Apr 2''' (logistic regression)

* Project 4, release date Apr 2, '''due date Apr 23''' (neural networks, svm, ensembles)

Projects and homework reinforce what you learned in class by materializing abstract concepts into practical problems. Every homework usually consists of a programming assignment. Schedule is as follows:

* Homework 1, release date Jan 13, '''due date Jan 20''' (probability)

* Project 1, release date Jan 22, '''due date Feb 5''' (decision trees)

* Project 2, release date Feb 26, '''due date Mar 12''' (naive bayes)

* Project 3, release date Mar 12, '''due date Apr 2''' (logistic regression)

* Project 4, release date Apr 2, '''due date Apr 23''' (neural networks, svm, ensembles)

Changed line 186 from:

!!! ~~Perceptrons~~

to:

!!! Perceptrons & Neural Networks

Deleted lines 187-188:

!!! Neural Networks

Added line 194:

*[[http://blog.cordiner.net/2010/12/02/eigenfaces-face-recognition-matlab/ | Eigenfaces tutorial]]

Changed lines 168-169 from:

* Readings~~. Tom Mitchell's~~ http://www.cs.cmu.edu/~tom/mlbook/NBayesLogReg.pdf.~~ Bishop, Chapter 3~~

to:

* Readings: Bishop, Chapter 3

* Tom Mitchell's http://www.cs.cmu.edu/~tom/mlbook/NBayesLogReg.pdf.

* Tom Mitchell's http://www.cs.cmu.edu/~tom/mlbook/NBayesLogReg.pdf.

Changed lines 151-154 from:

to:

* [[http://www.dmi.unict.it/~apulvirenti/agd/Qui86.pdf | Quinlan's Induction of Decision Trees]]

* [[http://isites.harvard.edu/fs/docs/icb.topic539621.files/lec7.pdf | ID3 and Overfitting]]

* [[http://select.cs.cmu.edu/class/10701-F09/recitations/recitation4_decision_tree.pdf | Prunning trees via chi-square]]

* [[http://isites.harvard.edu/fs/docs/icb.topic539621.files/lec7.pdf | ID3 and Overfitting]]

* [[http://select.cs.cmu.edu/class/10701-F09/recitations/recitation4_decision_tree.pdf | Prunning trees via chi-square]]

Added lines 161-162:

* [[https://engineering.purdue.edu/kak/Tutorials/Trinity.pdf | ML, MAP, and Bayesian — The Holy Trinity of Parameter Estimation and Data Prediction]]

* [[http://www.mi.fu-berlin.de/wiki/pub/ABI/Genomics12/MLvsMAP.pdf | MLE vs MAP]]

* [[http://www.mi.fu-berlin.de/wiki/pub/ABI/Genomics12/MLvsMAP.pdf | MLE vs MAP]]

Added line 175:

*[[http://melodi.ee.washington.edu/people/bilmes/mypapers/em.pdf | A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models ]]

Added lines 130-133:

!! SPECIAL ACCOMMODATIONS

If you need special accommodations or assistance, please contact the Accessibility Resource Center (http://as2.unm.edu/)

Changed line 54 from:

to:

Attendance to class is expected (read mandatory) and note taking encouraged. Important information (about exams, assignments, projects, policies) may be communicated only in the lectures. We may also cover additional material (not available in the book) during the lecture. If you miss a lecture, you should find what material was covered and if any announcement was made.

Added line 170:

*[[http://jellymatter.com/2013/10/01/intuitive-example-of-expectation-maximization/| Intuitive example of EM]]

Changed lines 160-161 from:

to:

* [[http://www.dcs.gla.ac.uk/~girolami/Machine_Learning_Module_2006/week_1/Lectures/wk_1.pdf | Linear regression notes from Mark Girolami]]

Changed lines 153-154 from:

* [[~~Tutorial on MLE estimation | ~~http://people.physics.anu.edu.au/~tas110/Teaching/Lectures/L3/Material/Myung03.pdf]]

to:

* [[http://people.physics.anu.edu.au/~tas110/Teaching/Lectures/L3/Material/Myung03.pdf | Tutorial on MLE estimation]]

Changed lines 153-154 from:

to:

* [[Tutorial on MLE estimation | http://people.physics.anu.edu.au/~tas110/Teaching/Lectures/L3/Material/Myung03.pdf]]

Changed line 198 from:

* Readings: Mitchell, Chapter 7

to:

* Readings: Mitchell, Chapter 7

Changed lines 39-41 from:

* Machine Learning, Tom Mitchell. ISBN-10: 0070428077 | ISBN-13: 978-0070428072 | Edition: 1 (optional)

* Pattern Recognition and Machine Learning, Christopher Bishop. ISBN-10: 0387310738 | ISBN-13: 978-0387310732 (optional)

* Pattern Recognition and Machine Learning, Christopher Bishop. ISBN-10: 0387310738 | ISBN-13: 978-0387310732 (optional)

to:

* '''Machine Learning, Tom Mitchell'''. ISBN-10: 0070428077 | ISBN-13: 978-0070428072 | Edition: 1 (optional)

* '''Pattern Recognition and Machine Learning, Christopher Bishop'''. ISBN-10: 0387310738 | ISBN-13: 978-0387310732 (optional)

* '''Pattern Recognition and Machine Learning, Christopher Bishop'''. ISBN-10: 0387310738 | ISBN-13: 978-0387310732 (optional)

Changed lines 36-41 from:

to:

!!! Textbook

Most lectures will be based on the following optional textbooks:

* Machine Learning, Tom Mitchell. ISBN-10: 0070428077 | ISBN-13: 978-0070428072 | Edition: 1 (optional)

* Pattern Recognition and Machine Learning, Christopher Bishop. ISBN-10: 0387310738 | ISBN-13: 978-0387310732 (optional)

Most lectures will be based on the following optional textbooks:

* Machine Learning, Tom Mitchell. ISBN-10: 0070428077 | ISBN-13: 978-0070428072 | Edition: 1 (optional)

* Pattern Recognition and Machine Learning, Christopher Bishop. ISBN-10: 0387310738 | ISBN-13: 978-0387310732 (optional)

Deleted line 197:

Changed lines 106-107 from:

I value student's opinions regarding the course and I will take them in consideration to make this course as exciting and engaging as possible. Thus, through the semester I will ask students formal and informal feedback. Formal feedback includes short surveys on my teaching effectiveness, preferred teaching methods, and pace of the class. Informal feedback will be in the form of ~~FB ~~polls or in-class questions regarding learning preferences. You can also leave anonymous feedback in the form of a note in my departmental mail box, under my ~~Lab~~ door (~~Smith 203~~), or %target=_blank%[[https://docs.google.com/spreadsheet/viewform?fromEmail=true&formkey=dHZvUElpWXJsTXV6RTAwZGlCTkYtQVE6MQ | using this form]]. Remember that it is in the best interest of the class if you bring up to my attention if something is not working properly (e.g the pace of the class is too slow, the projects are boring, my teaching style is not effective) so that I can make the corrective steps.

to:

I value student's opinions regarding the course and I will take them in consideration to make this course as exciting and engaging as possible. Thus, through the semester I will ask students formal and informal feedback. Formal feedback includes short surveys on my teaching effectiveness, preferred teaching methods, and pace of the class. Informal feedback will be in the form of polls or in-class questions regarding learning preferences. You can also leave anonymous feedback in the form of a note in my departmental mail box, under my office door (FEC 325), or %target=_blank%[[https://docs.google.com/spreadsheet/viewform?fromEmail=true&formkey=dHZvUElpWXJsTXV6RTAwZGlCTkYtQVE6MQ | using this form]]. Remember that it is in the best interest of the class if you bring up to my attention if something is not working properly (e.g the pace of the class is too slow, the projects are boring, my teaching style is not effective) so that I can make the corrective steps.

Changed lines 129-193 from:

to:

!! SCHEDULE

!!! Introduction

* Readings: Mitchell, Chapter 1

!!! Inductive Learning

* Readings: Mitchell, Chapter 1

!!! Concept Learning

* Readings: Mitchell, Chapter 2

!!! Decision Trees

* Readings: Mitchell, Chapter 3

!!! Probability Review

* Readings: Bishop, Chapters 1 and 2

!!! Bayesian vs MLE Approach

* Readings: Bishop, Chapters 1 and 2

!!! Naive Bayes

* Readings: Tom Mitchell's book chapter http://www.cs.cmu.edu/~tom/mlbook/NBayesLogReg.pdf

!!! Linear and Logistic Regression

* Readings. Tom Mitchell's http://www.cs.cmu.edu/~tom/mlbook/NBayesLogReg.pdf. Bishop, Chapter 3

!!! Bayesian networks: Representation

* Readings: Bishop, Chapter 8

!!! Bayesian networks: Inference

* Readings: Bishop, Chapter 8

!!! EM algorithm and Gaussian Mixture models

* Readings: Bishop, Chapter 9

!!! Hidden Markov models

* Readings: Bishop, Chapter 13

!!! Perceptrons

* Readings: Mitchell, Chapter 4

!!! Neural Networks

* Readings: Bishop Chapter 5

!!! Principal Component Analysis

* Readings: Bishop Chapter 12

!!! Introduction to Kernel functions

* Readings: Bishop Chapter 6

!!! Support Vector Machines

* Readings: Bishop, Chapter 7

!!! Instance Based Learning

* Readings: Mitchell, Chapter 8

!!! Bias/Variance Tradeoff, Boosting and Bagging

* Readings: Bishop, Chapter 14

!!! Unsupervised Learning and Clustering

* Readings: Bishop, Chapter 9

!!! Learning Theory

* Readings: Mitchell, Chapter 7

!!! Introduction

* Readings: Mitchell, Chapter 1

!!! Inductive Learning

* Readings: Mitchell, Chapter 1

!!! Concept Learning

* Readings: Mitchell, Chapter 2

!!! Decision Trees

* Readings: Mitchell, Chapter 3

!!! Probability Review

* Readings: Bishop, Chapters 1 and 2

!!! Bayesian vs MLE Approach

* Readings: Bishop, Chapters 1 and 2

!!! Naive Bayes

* Readings: Tom Mitchell's book chapter http://www.cs.cmu.edu/~tom/mlbook/NBayesLogReg.pdf

!!! Linear and Logistic Regression

* Readings. Tom Mitchell's http://www.cs.cmu.edu/~tom/mlbook/NBayesLogReg.pdf. Bishop, Chapter 3

!!! Bayesian networks: Representation

* Readings: Bishop, Chapter 8

!!! Bayesian networks: Inference

* Readings: Bishop, Chapter 8

!!! EM algorithm and Gaussian Mixture models

* Readings: Bishop, Chapter 9

!!! Hidden Markov models

* Readings: Bishop, Chapter 13

!!! Perceptrons

* Readings: Mitchell, Chapter 4

!!! Neural Networks

* Readings: Bishop Chapter 5

!!! Principal Component Analysis

* Readings: Bishop Chapter 12

!!! Introduction to Kernel functions

* Readings: Bishop Chapter 6

!!! Support Vector Machines

* Readings: Bishop, Chapter 7

!!! Instance Based Learning

* Readings: Mitchell, Chapter 8

!!! Bias/Variance Tradeoff, Boosting and Bagging

* Readings: Bishop, Chapter 14

!!! Unsupervised Learning and Clustering

* Readings: Bishop, Chapter 9

!!! Learning Theory

* Readings: Mitchell, Chapter 7

Changed line 21 from:

* Email: '''~~lqcobra~~@~~gmail~~.~~com~~'''

to:

* Email: '''lukey11@unm.edu'''

Changed lines 8-9 from:

to:

* Piazza link: '''piazza.com/unm/spring2014/cs429529'''

Changed lines 17-23 from:

!!! Teaching ~~Assistant~~

*Name

~~* Email~~:

~~* Office:~~

* Office hours:

*

* Office hours

to:

!!! Teaching Assistants

Name: '''Qi Lu'''

* Email: '''lqcobra@gmail.com'''

* Office: '''FEC 116'''

* Office hours: '''Wed 1:00-3:00'''

Name: '''Dejun Jiang'''

* Email: '''pwinter@unm.edu'''

* Office: '''FEC 126'''

* Office hours: '''Wed 10:00-12:00'''

Name: '''Qi Lu'''

* Email: '''lqcobra@gmail.com'''

* Office: '''FEC 116'''

* Office hours: '''Wed 1:00-3:00'''

Name: '''Dejun Jiang'''

* Email: '''pwinter@unm.edu'''

* Office: '''FEC 126'''

* Office hours: '''Wed 10:00-12:00'''

Changed lines 117-119 from:

If you need special accommodations or assistance, please contact the Office of Disabilities Support Services at the University of Delware (%target=_blank%http://www.udel.edu/DSS/)

to:

Changed line 6 from:

* Building and Room: '''~~Mechanical Engineering ~~'''

to:

* Building and Room: '''Dane Smith Hall 225'''

Added lines 61-68:

* Students enrolled in the '''429''' section can work in groups of 3

* Students enrolled in the '''529''' section can work in groups of 2

!!! Final project:

Projects are one of the most important learning tools of this class. The final project is entirely to the discretion of the student (upon instructor approval). Students would be free to explore a problem of their interest and propose their own solution. You must turn in only code written by you. Under no circumstance you should use code downloaded from Internet since this violation will result in serious penalties.

* Students enrolled in the '''529''' section can work in groups of 2

!!! Final project:

Projects are one of the most important learning tools of this class. The final project is entirely to the discretion of the student (upon instructor approval). Students would be free to explore a problem of their interest and propose their own solution. You must turn in only code written by you. Under no circumstance you should use code downloaded from Internet since this violation will result in serious penalties.

Deleted lines 71-78:

!!! Final project:

Projects are one of the most important learning tools of this class. The final project is entirely to the discretion of the student (upon instructor approval). Students would be free to explore a problem of their interest and propose their own solution. You must turn in only code written by you. Under no circumstance you should use code downloaded from Internet since this violation will result in serious penalties.

* Students enrolled in the '''429''' section can work in groups of 2

* Students enrolled in the '''529''' section must work individually

Changed lines 76-78 from:

** '''Due date: May ~~8~~'''

to:

** '''Due date: May 5'''

Changed lines 44-45 from:

!! ~~Assignments~~

to:

!! ASSIGNMENTS

Changed lines 86-87 from:

!! ~~Exam: ~~

to:

!! EXAM

Changed lines 96-97 from:

!!~~! Feedback:~~

to:

!! FEEDBACK

Added line 103:

Added lines 1-28:

! CS 429/529

!! COURSE INFORMATION

* Class Time: '''TR 9:30-10:45 AM'''

* Building and Room: '''Mechanical Engineering '''

* Prerequisites: '''362 and STAT 345 and (MATH 314 or MATH 321)'''

!!! Instructor

* '''Trilce Estrada''', Assistant Professor

* Email: '''estrada@cs.unm.edu'''

* Office: ''' FEC 325 '''

* Office hours: '''Tuesday 11:00-1:00''' and '''Thursday 11:00-12:00'''

!!! Teaching Assistant

* Name

* Email:

* Office:

* Office hours:

!! Course description:

Introduction to principles and practice of systems that improve performance through experience. Topics include statistical learning framework, supervised and unsupervised learning, performance evaluation and empirical methodology; design tradeoffs.

!! COURSE INFORMATION

* Class Time: '''TR 9:30-10:45 AM'''

* Building and Room: '''Mechanical Engineering '''

* Prerequisites: '''362 and STAT 345 and (MATH 314 or MATH 321)'''

!!! Instructor

* '''Trilce Estrada''', Assistant Professor

* Email: '''estrada@cs.unm.edu'''

* Office: ''' FEC 325 '''

* Office hours: '''Tuesday 11:00-1:00''' and '''Thursday 11:00-12:00'''

!!! Teaching Assistant

* Name

* Email:

* Office:

* Office hours:

!! Course description:

Introduction to principles and practice of systems that improve performance through experience. Topics include statistical learning framework, supervised and unsupervised learning, performance evaluation and empirical methodology; design tradeoffs.

Changed lines 1-2 from:

------~~---~~

to:

------

Changed lines 75-79 from:

* ~~[[~~Participation~~]] 15 pts~~

* [[Homework]] ~~ 35~~ pts

*~~[[Project]]~~ 35 pts

*~~[[Exam]]~~ 15 pts

* [[Homework]]

*

*

to:

* Participation 15 pts

* Homework 35 pts

* Project 35 pts

* Exam 15 pts

* Homework 35 pts

* Project 35 pts

* Exam 15 pts

Changed lines 1-92 from:

to:

---------

!! POLICIES

!!! Academic honesty:

Unless otherwise specified, you must write/code your own homework assignments. You cannot use the web to find answers to any assignment. If you do not have time to complete an assignment, it is better to submit your partial solutions than to get answers from someone else. Cheating students will be prosecuted according to University guidelines. Students should get acquainted with their rights and responsibilities as explained in the Student Code of Conduct

* %target=_blank%http://dos.unm.edu/student-conduct/academic-integrityhonesty.html

!!! Class attendance:

Since this is a core course, attendance to class is expected (read mandatory) and note taking encouraged. Important information (about exams, assignments, projects, policies) may be communicated only in the lectures. We may also cover additional material (not available in the book) during the lecture. If you miss a lecture, you should find what material was covered and if any announcement was made.

!! Assignments

!!! Homeworks:

Homeworks reinforce what you learned in class by materializing abstract concepts into practical problems. Every homework usually consists of a programming assignment. Homeworks schedule is as follows:

* Homework 1, release date Jan 30, '''due date Feb 13'''

* Homework 2, release date Feb 27, '''due date Mar 13'''

* Homework 3, release date Mar 13, '''due date Apr 3'''

* Homework 4, release date Apr 3, '''due date Apr 17'''

* Homework 5, release date Apr 17, '''due date May 1'''

You can discuss homework with other classmates but all the code have to be written by you. When significant portions of the homework are done in groups, you are expected to acknowledge all the other students that participated in the discussion and outline what you learned specifically from it.

* Homework has to be uploaded to UNM Learn and late homework will be accepted only within the following policy:

** Every student has 6 free days to be used at their own discretion across the multiple homeworks

** Once a student has used all of his/her free days, no other late homework will be accepted

* Students enrolled in the '''429''' section can work in groups of 2

* Students enrolled in the '''529''' section must work individually

!!! Final project:

Projects are one of the most important learning tools of this class. The final project is entirely to the discretion of the student (upon instructor approval). Students would be free to explore a problem of their interest and propose their own solution. You must turn in only code written by you. Under no circumstance you should use code downloaded from Internet since this violation will result in serious penalties.

* Students enrolled in the '''429''' section can work in groups of 2

* Students enrolled in the '''529''' section must work individually

The project has the following deliverables:

* Proposal: maximum 1 page of project proposal, why the problem is important, what has been done so far in the field, and what are the expected outcomes

** '''Due date: Feb 27'''

* Poster and report

** '''Due date: May 8'''

!!! Daily assignments and quizzes

You can expect to have simple exercises and quizzes every meeting. These daily assignments will be done in groups specified by the instructor and they will account for your participation grade (15% of your final grade)

!! Exam:

Exams are our formal evaluation tool. In the exams you will be tested with respect to the learning goals of this course (see the schedule below for the list of learning goals). Exams will comprise a mix of practical exercises and concepts. I don't encourage you to learn concepts and definitions by hart, but to be able to explain them with your own words and to place these concepts into the broader context they belong to. There will be only one '''midterm exam on April 8th'''

'''The exam is open notes, but only personal, hand-written notes are accepted'''. Restrictions in this matter include (but are not limited): you cannot download notes from Internet, you cannot use the electronic notes of the course, and you cannot photocopy notes from your classmates. In fact, the key point is that they must be '''your own hand-written notes''' because I expect you to reinforce what you learned in class by writing down key concepts.

!!! Feedback:

I value student's opinions regarding the course and I will take them in consideration to make this course as exciting and engaging as possible. Thus, through the semester I will ask students formal and informal feedback. Formal feedback includes short surveys on my teaching effectiveness, preferred teaching methods, and pace of the class. Informal feedback will be in the form of FB polls or in-class questions regarding learning preferences. You can also leave anonymous feedback in the form of a note in my departmental mail box, under my Lab door (Smith 203), or %target=_blank%[[https://docs.google.com/spreadsheet/viewform?fromEmail=true&formkey=dHZvUElpWXJsTXV6RTAwZGlCTkYtQVE6MQ | using this form]]. Remember that it is in the best interest of the class if you bring up to my attention if something is not working properly (e.g the pace of the class is too slow, the projects are boring, my teaching style is not effective) so that I can make the corrective steps.

----------

!! GRADING

* [[Participation]] 15 pts

* [[Homework]] 35 pts

* [[Project]] 35 pts

* [[Exam]] 15 pts

Grades will be based on your earned points, following this grade scale. You need to get the specified number of points or more to obtain the grade from the same column. Scores will be rounded to the closest integer value.

[@

A A- B+ B B- C+ C C- D+ D D- F

95 90 87 83 80 77 73 70 67 63 60 <60

@]

* Incomplete can be assigned only for a documented medical reason

!! SPECIAL ACCOMODATIONS

If you need special accommodations or assistance, please contact the Office of Disabilities Support Services at the University of Delware (%target=_blank%http://www.udel.edu/DSS/)

----------

!! SCHEDULE

!! POLICIES

!!! Academic honesty:

Unless otherwise specified, you must write/code your own homework assignments. You cannot use the web to find answers to any assignment. If you do not have time to complete an assignment, it is better to submit your partial solutions than to get answers from someone else. Cheating students will be prosecuted according to University guidelines. Students should get acquainted with their rights and responsibilities as explained in the Student Code of Conduct

* %target=_blank%http://dos.unm.edu/student-conduct/academic-integrityhonesty.html

!!! Class attendance:

Since this is a core course, attendance to class is expected (read mandatory) and note taking encouraged. Important information (about exams, assignments, projects, policies) may be communicated only in the lectures. We may also cover additional material (not available in the book) during the lecture. If you miss a lecture, you should find what material was covered and if any announcement was made.

!! Assignments

!!! Homeworks:

Homeworks reinforce what you learned in class by materializing abstract concepts into practical problems. Every homework usually consists of a programming assignment. Homeworks schedule is as follows:

* Homework 1, release date Jan 30, '''due date Feb 13'''

* Homework 2, release date Feb 27, '''due date Mar 13'''

* Homework 3, release date Mar 13, '''due date Apr 3'''

* Homework 4, release date Apr 3, '''due date Apr 17'''

* Homework 5, release date Apr 17, '''due date May 1'''

You can discuss homework with other classmates but all the code have to be written by you. When significant portions of the homework are done in groups, you are expected to acknowledge all the other students that participated in the discussion and outline what you learned specifically from it.

* Homework has to be uploaded to UNM Learn and late homework will be accepted only within the following policy:

** Every student has 6 free days to be used at their own discretion across the multiple homeworks

** Once a student has used all of his/her free days, no other late homework will be accepted

* Students enrolled in the '''429''' section can work in groups of 2

* Students enrolled in the '''529''' section must work individually

!!! Final project:

Projects are one of the most important learning tools of this class. The final project is entirely to the discretion of the student (upon instructor approval). Students would be free to explore a problem of their interest and propose their own solution. You must turn in only code written by you. Under no circumstance you should use code downloaded from Internet since this violation will result in serious penalties.

* Students enrolled in the '''429''' section can work in groups of 2

* Students enrolled in the '''529''' section must work individually

The project has the following deliverables:

* Proposal: maximum 1 page of project proposal, why the problem is important, what has been done so far in the field, and what are the expected outcomes

** '''Due date: Feb 27'''

* Poster and report

** '''Due date: May 8'''

!!! Daily assignments and quizzes

You can expect to have simple exercises and quizzes every meeting. These daily assignments will be done in groups specified by the instructor and they will account for your participation grade (15% of your final grade)

!! Exam:

Exams are our formal evaluation tool. In the exams you will be tested with respect to the learning goals of this course (see the schedule below for the list of learning goals). Exams will comprise a mix of practical exercises and concepts. I don't encourage you to learn concepts and definitions by hart, but to be able to explain them with your own words and to place these concepts into the broader context they belong to. There will be only one '''midterm exam on April 8th'''

'''The exam is open notes, but only personal, hand-written notes are accepted'''. Restrictions in this matter include (but are not limited): you cannot download notes from Internet, you cannot use the electronic notes of the course, and you cannot photocopy notes from your classmates. In fact, the key point is that they must be '''your own hand-written notes''' because I expect you to reinforce what you learned in class by writing down key concepts.

!!! Feedback:

I value student's opinions regarding the course and I will take them in consideration to make this course as exciting and engaging as possible. Thus, through the semester I will ask students formal and informal feedback. Formal feedback includes short surveys on my teaching effectiveness, preferred teaching methods, and pace of the class. Informal feedback will be in the form of FB polls or in-class questions regarding learning preferences. You can also leave anonymous feedback in the form of a note in my departmental mail box, under my Lab door (Smith 203), or %target=_blank%[[https://docs.google.com/spreadsheet/viewform?fromEmail=true&formkey=dHZvUElpWXJsTXV6RTAwZGlCTkYtQVE6MQ | using this form]]. Remember that it is in the best interest of the class if you bring up to my attention if something is not working properly (e.g the pace of the class is too slow, the projects are boring, my teaching style is not effective) so that I can make the corrective steps.

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!! GRADING

* [[Participation]] 15 pts

* [[Homework]] 35 pts

* [[Project]] 35 pts

* [[Exam]] 15 pts

Grades will be based on your earned points, following this grade scale. You need to get the specified number of points or more to obtain the grade from the same column. Scores will be rounded to the closest integer value.

[@

A A- B+ B B- C+ C C- D+ D D- F

95 90 87 83 80 77 73 70 67 63 60 <60

@]

* Incomplete can be assigned only for a documented medical reason

!! SPECIAL ACCOMODATIONS

If you need special accommodations or assistance, please contact the Office of Disabilities Support Services at the University of Delware (%target=_blank%http://www.udel.edu/DSS/)

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!! SCHEDULE