Instructor: Anil Damle


Office hours: Tuesdays 3:15 - 4:30 pm (Booking Link) in 423 Gates Hall

Instructor: Wen Sun


Office hours: Thursdays 2 - 3 pm in Gates Hall 416b

Lectures: Tuesday and Thursday from 9:40 am to 10:55 am in Bailey Hall 101.

Course staff office hours: Calendar link

Course overview: The course provides an introduction to machine learning, focusing on supervised learning and its theoretical foundations. Topics include regularized linear models, boosting, kernels, deep networks, generative models, online learning, and ethical questions arising in ML applications.

Prerequisites: probability theory (e.g. BTRY 3080, ECON 3130, MATH 4710, ENGRD 2700), linear algebra (e.g. MATH 2940), calculus (e.g. MATH 1920), and programming proficiency (e.g. CS 2110).

Course logistics: For enrolled students the companion Canvas page serves as a hub for access to Ed Discussions (the course forum), Vocareum (for course projects), Gradescope (for HWs), and paper comprehension quizzes. If you are enrolled in the course you should automatically have access to the site. Please let us know if you are unable to access it.

Homework, projects, and exams

Your grade in this course is comprised of three components: homework, exams, and projects. Please also read through the given references in concert with the lectures.


Final grades are based on homework assignments, programming projects, and the exams. For the 5780 level version of the course the research comprehension quizzes will also factor in.

For CS 4780 your final grade consists of: For CS 5780 your final grade consists of:

Undergraduates enrolled in 4780 may choose to do the paper comprehension assignments; if completed you will receive the higher of your two grades between the above schemes.


A tentative schedule follows, and includes the topics we will be covering, relevant reference material, and assignment information. It is quite possible the specific topics covered on a given day will change slightly. This is particularly true for the lectures in the latter part of the course, and this schedule will be updated as necessary.

Date Topic References Notes/assignments
8/23 Introduction PML: 1.1; ESL: Ch. 1; and PPA: Ch. 1
8/25 ML Basics Lecture Notes, Slides, Annotated Slides, PML: 1.2, and ESL: 2.1 and 2.2.
8/30 K Nearest Neighbors and the curse of dimensionality Lecture Notes, Slides, Annotated Slides, PML: 16.1 5780: Cover and Hart 1967
9/1 Clustering: K-means Lecture Notes, Slides, Annotated Slides, ESL: 14.3.6 and 14.3.7, and PML: 21.3
9/6 Principal Component Analysis Lecture Notes, Slides, Annotated Slides, PML: 20.1, ESL: 14.5.1 and 14.5.2
9/8 The Perceptron Lecture Notes and Wikipedia article
9/13 MLE and MAP Lecture Notes, Slides, Annotated Slides, Nice Youtube video for MLE and MAP. Ben Taskar's lecture notes. ESL: 8.2.2-8.3 Homework 1 due; Project 1 due
9/15 Naive Bays Lecture Notes, ESL: 6.6.3, and Tom Mitchell's book chapter Cover and Hart reading quiz due
9/20 Logistic Regression Lecture Notes, ESL: 4.4, and PML: 10.1, 0.2, and 10.3
9/22 Gradient descent and Newton's method Lecture Notes, Alternative Lecture Notes, PML: 8.1, 8.2, and 8.3 Homework 2 due
9/27 Assorted, relevant topics in optimization Lecture Notes, Alternative Lecture Notes, PML: 8.1, 8.2, 8.3, and 8.4 (specifically, see PML 8.4 for SGD) Project 2 due
9/29 Linear regression Lecture Notes;, Slides, Annotated Slides,PML 11.1, 11.2,11.3; Tom Mitchell’s book chapter on Naive Bays and Linear Regression; and ESL 3.2
10/4 Support Vector Machine Lecture Notes, Slides, Annotated Slides Project 3 due
10/6 Empirical Risk Minimization Lecture Notes, Slides, Annotated Slides, PML 4.3, 5.4 Homework 3 due
10/11 Fall break, no class
10/13 Midterm Jeopardy Homework 4 due 10/14
10/18 Exam Review Evening prelim
10/20 Bias and Variance Tradeoff Lecture Notes, Slides, Annotated Slides, Note
10/25 Bias and Variance Tradeoff and Model Selection Lecture Notes, Slides, Annotated Slides, Note Project 4 due
10/27 Kernels, part 1 Lecture Notes, PML: 17.1
11/1 Kernels, part 2 Lecture Notes, PML: 17.3
11/3 Classification and regression trees, part 1 Lecture Notes Homework 5 due; Project 5 due
11/8 Classification and regression trees, part 2 Lecture Notes
11/10 Ensemble Methods: Bagging & random forest Lecture Notes, Slides, Annotated Slides
11/15 Ensemble Methods: Boosting Lecture Notes, Slides, Annotated Slides Project 6 due
11/17 Neural Network Lecture Notes, Slides, Annotated Slides Homework 6 due
11/22 Neural Network: backpropagation Note on Backpropagation, Slides,Annotated Slides, Lecture Notes
11/24 Thanksgiving, no class
11/29 Neural networks: convolutional and residual layers PML: 14.1, 14.2, 14.3 and Lecture Notes Project 7 due; Homework 7 due
12/1 Neural networks: transformers PML: 15.4 and 15.5, and Lecture Notes Project 8 due and Homework 8 due 12/5
12/14 Final Exam 2 pm in Statler 185 and 196


While this course does not explicitly follow a specific textbook, there are several that are very useful references to supplement the course.


We will not be explicitly following any single textbook in this course. Nevertheless, the books by Golub and Van Loan, and Trefethen and Bau collectively cover the material for the course and are recommended. Most suggested readings are assigned out of these two texts. Three additional texts are provided that complement these texts and are useful for further study (or to gain another perspective).

Additional references

Background references


Course policies


You should expect and demand to be treated by your classmates and the course staff with respect. You belong here, and we are here to help you learn and enjoy this course. If any incident occurs that challenges this commitment to a supportive and inclusive environment, please let the instructors know so that the issue can be addressed. We are personally committed to this, and subscribe to the Computer Science Department’s Values of Inclusion. [Statement reproduced with permission from Dan Grossman.]

Mental health resources

Cornell University provides a comprehensive set of mental health resources and the student group Body Positive Cornell has put together a flyer outlined the resources available.


You are encouraged to actively participate in class. This can take the form of asking questions in class, responding to questions to the class, and actively asking/answering questions on the online discussion board.

Collaboration policy

Students are free to share code and ideas within their stated project/homework group for a given assignment, but should not discuss details about an assignment with individuals outside their group. The midterm and final exam are individual assignments and must be completed by yourself.

Academic integrity

The Cornell Code of Academic Integrity applies to this course.


In compliance with the Cornell University policy and equal access laws, we are available to discuss appropriate academic accommodations that may be required for student with disabilities. Requests for academic accommodations are to be made during the first three weeks of the semester, except for unusual circumstances, so arrangements can be made. Students are encouraged to register with Student Disability Services to verify their eligibility for appropriate accommodations.

COVID-19 considerations

While many aspects of this course are built with flexibility in mind, if situations arise that may require additional accommodations please reach out to the instructors to discuss potential arrangements.