CS 429/529

Course Information

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

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.


Most lectures will be based on the following optional textbooks:

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


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

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.

Class attendance:

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.

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.

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.

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



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 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)

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 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:
    • Every student has 4 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
  • Projects can be made in teams as follows:
    • 2 graduate students
    • 1 graduate student + 2 undergraduate students
    • 4 undergraduate students

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 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.

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 (15% of your final grade)


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.

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


  • Participation 15 pts
  • Quizzes 15 pts
  • Projects 40 pts
  • Exams 30 pts

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.

A	A-	B+	B	B-	C+	C	C-	D+	D	D-	F
97	95	90	85	80	77	73	70	67	63	60	<60
  • Incomplete can be assigned only for a documented medical reason


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.


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 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.



  • Readings: Mitchell, Chapter 1


Random Forests, Boosting and Bagging

Probability Review

Bayesian vs MLE Approach

Naive Bayes

Logistic Regression

Linear Regression

Bias/Variance Tradeoff

  • Readings: Bishop, Chapter 14

Instance-Based Learning

  • Readings: Mitchell, Chapter 8

Dimensionality reduction, PCA, SVD

Data representation LSI, ICA, Autoencoders

Perceptrons & Neural Networks

Activation and loss functions

Optimization strategies and dealing with overfitting

Neural Architectures

Convolutional Neural Networks

RNNs and LSTMs


Deep Learning

Style Transfer

Generative Adversarial Networks


Introduction to Kernel functions

  • Optional: Bishop Chapter 6

Support Vector Machines

Bayesian networks: Representation & Inference

Hidden Markov Models

Unsupervised Learning and Clustering

  • Readings: Bishop, Chapter 9

EM algorithm and Gaussian Mixture models

Learning Theory

  • Readings: Mitchell, Chapter 7

Best practices in ML