Advanced Machine Learning: Theory and Methods

10-716, Spring 2022
Zoom, Tue & Thurs 3:05PM - 4:25PM

Instructor Pradeep Ravikumar (pradeepr at cs dot cmu dot edu)

Teaching Assistants Saurabh Garg (sgarg2 at andrew dot cmu dot edu)
Bingbin Liu (bingbinl at andrew dot cmu dot edu)
Akshunn Jindal (akshunnj at andrew dot cmu dot edu)
Xinyue Chen (xinyuech at andrew dot cmu dot edu)

Office Hours The office hours of the Instructors/TAs will be posted on Piazza.

Key Course Links Syllabus. Piazza. Gradescope. Review Material.

Discussion, Announcements We will use Piazza for announcements, providing resource materials, as well as the discussion board for the class.

Textbooks Lecture notes will be posted for each class, which will be largely self-contained.

Date Topic Readings Note
18-Jan Decision Theory: Estimation Principles, Optimality
20-Jan Decision Theory: Perils of Likelihood Principle
25-Jan Nonparametric Bayes
27-Jan Nonparametric Bayes
28-Jan Recitation :Saurabh
1-Feb Nonparametric Density Estimation HW1 out
3-Feb Nonparametric Density Estimation
8-Feb Nonparametric Regression
10-Feb Nonparametric Regression
11-Feb Recitation :Bingbin
15-Feb Nonparametric Regression HW1 due; HW2 out
17-Feb Nonparametric Classification
22-Feb Nonparametric Greedy & Boosting
24-Feb Nonparametric Greedy & Boosting
25-Feb Recitation :Saurabh
1-Mar Games & Boosting HW2 due, HW3 out
3-Mar Games & Boosting
8-Mar Spring Break
10-Mar Spring Break
15-Mar Deep Neural Networks & Kernels
17-Mar Deep Neural Networks & Kernels Project Proposal due
18-Mar Recitation :Bingbin
22-Mar Deep Density Estimation HW3 due; HW4 out
24-Mar Deep Density Estimation
29-Mar Optimal Transport
31-Mar Optimal Transport
1-Apr Recitation :Saurabh
5-Apr High Dimensional Regression HW4 due; HW5 out
7-Apr Spring Carnival
12-Apr Clustering Project Milestone due
14-Apr Clustering
15-Apr Recitation :Bingbin
19-Apr Causality HW5 due
21-Apr High Dimensional Regression
26-Apr Class Project Presentations
28-Apr Class Project Presentations
5-May Final Project Report Due

Homeworks There will be five homework assignments, approximately evenly spaced throughout the semester. The assignments will be posted on the course website, and on Piazza. We will use Gradescope for submitting, and grading assignments. You will get a late day quota of 10 days, which you can distribute among the five homeworks as you wish, subject to a maximum of 3 days per homework. Homeworks submitted after your late day quota will lose all points. The homework schedule is posted right at the beginning of the semester, so please plan in advance. We expect you to use the late day quota for conference deadlines and events of the like, so we cannot provide an additional extension for such cases. In the case of an emergency (sudden sickness, family problems, etc.), we can give you a reasonable extension. But we emphasize that this is reserved for true emergencies.

Class Project There will be a class project. You can form groups of up to 3 students.

Grading 75% Homeworks, 25% Project