ML

Syllabus

ML.Syllabus History

Hide minor edits - Show changes to output

Changed line 216 from:
* [[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]]
Added lines 212-219:

!! 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:
!!! 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)]]

to:
Changed lines 192-212 from:
* 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)]]

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]]
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]]
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:
!!! Bayesian networks: Representation & Inference
* Readings: Bishop, Chapter 8

to:
Changed lines 147-148 from:
!!! Bayesian Networks
to:
!!! Bayesian networks: Representation & Inference
* 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 ]]

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

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
to:
* Participation  15 pts
* Quizzes 15 pts
Added lines 138-141:
!!! Bayesian networks: Representation & Inference
* Readings: Bishop, Chapter 8

Added lines 147-149:
!!! Bias/Variance Tradeoff, Boosting and Bagging
* Readings: Bishop, Chapter 14

Deleted lines 184-192:
!!! Bayesian networks: Representation
* Readings: Bishop, Chapter 8

!!! Bayesian networks: Inference
* Readings: Bishop, Chapter 8

!!! Hidden Markov models
* Readings: Bishop, Chapter 13

Deleted lines 187-188:
!!! Bias/Variance Tradeoff, Boosting and Bagging
* 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:
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:
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
May 04, 2017, at 11:15 AM EST by 129.24.247.210 -
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/)
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.
Added lines 152-156:

!!! Deep Learning
* [[https://www.youtube.com/watch?v=W15K9PegQt0 | Andrew Ng on Deep Learning]]
* [[http://deeplearning.net/demos/ | DL Demos]]

Changed line 158 from:
!!! 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
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]]

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 ]]
Deleted lines 168-172:
!!! 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 175-179:
!!! Introduction to Kernel functions
* 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.


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)



Changed lines 95-96 from:
* Participation  15 pts
* Homework 20 pts
to:
* Participation  10 pts
* Homework 10 pts
* Quizzes 10
pts
Changed lines 99-102 from:
* Paper     5 pts
* Exams 20
pts

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]]
May 05, 2015, at 11:35 AM EST by 64.106.39.101 -
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'''.
May 05, 2015, at 11:33 AM EST by 64.106.39.101 -
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

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:
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:
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


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.
Changed lines 80-81 from:
* Homework 30 pts
* Projects  35 pts
to:
* Homework 20 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)
to:

* Project 1, release date Jan 20, '''due date Feb 3''' (decision trees)
Changed lines 57-70 from:
!!! 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 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
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  -''' ()

Changed lines 3-31 from:
!! COURSE INFORMATION

* 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'''
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)
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.
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]]

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]]
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:
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.
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)
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)
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)

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:
!! SCHEDULE
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

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
:

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'''


Changed lines 117-119 from:
!! 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/)

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.

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.

Changed lines 1-2 from:
---------
to:
------
Changed lines 75-79 from:
* [[Participation]] 15 pts
* [[Homework]]
35 pts
* [[Project]] 35 pts
* [[Exam]] 15 pts
to:
* Participation   15 pts
* Homework 35 pts
* Project  35 pts
* Exam
15 pts
Changed lines 1-92 from:
In construction
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
Added line 1:
In construction