About the Course

When talking to some students, one of the questions I get the most is how to get started in research as an undergrad. This course is designed to be the first step towards in-depth understanding and rigorous analyses in both theoretical and empirical machine learning.

This course will cover advanced machine learning, from VC dimension to Generative AI. It will be divided into two parts: theoretical and empirical. In the first we will cover topics such as VC dimension, Rademacher complexity, ERM, generalization bounds, and optimization basics. Next we will cover the components and development of advanced ML systems.

Prerequisites: Students entering the class should be comfortable with programming and should have a pre-existing working knowledge of linear algebra (MATH 308), vector calculus (MATH 126), probability and statistics (CSE 312/STAT390), and algorithms. Knowledge of machine learning at the level of CSE446 is highly recommended.

Past offering of this course by Ludwig Schmidt: link

Staff: See the Staff Info page for information about the staff

Lectures

Lecture time and place: Tuesdays, Thursdays 10:00 -- 11:20am, CSE2 G10 (The topics below are tentative)

• Lecture 1: Introduction
• Lecture 2: Introduction to learning theory
• Lecture 3: Introduction to learning theory
• Lecture 4: Introduction to learning theory
• Lecture 5: Convergence of Gradient Descent.
• Lecture 6: Convergence of Gradient Descent.
• Lecture 7: Continued analysis of SGD, modern optimizers.
• Lecture 8: Generalization bounds and online learning.
• Lecture 9: Rademacher complexity
• Lecture 10: Introduction to language modelling
• Lecture 10:
• Lecture 11:
• Lecture 12:
• Lecture 13:
• Lecture 14:
• Lecture 15:
• Lecture 16:
• Lecture 17:
• Lecture 18:
• Lecture 19:
• Lecture 20:

Assignments

Projects

Grading:

For students enrolled in CSE 493S, your grade will be determined by:

• 25% assignment 1
• 25% assignment 2
• 50% final project

For students enrolled in CSE 599, your grade will be determined by:

• 20% assignment 1
• 20% assignment 2
• 10% reading assignment
• 50% final project

Where to get help

• EdStem discussion board:
  • Public/Anonymous Posts
    • Questions like, "Is there a typo in the homework?", "What does this notation mean?", "Is this an accurate description of how this works?".
    • Questions that are not of a personal nature should be posted to the discussion board.
  • Private Posts
    • Questions involving your own code should be posted privately to the EdStem discussion board, not office hours.
    • Personal concerns (like "I was in the hospital", "Laptop was stolen").
• Course staff email cse493s-staff@cs.washington.edu
  • Personal concerns (like "I was in the hospital", "Laptop was stolen") you aren't comfortable sharing with the entire staff. We highly recommend posting privately to EdStem if you are comfortable.
  • Please direct all course-related inquiries to cse493s-staff@cs.washington.edu or EdStem. Please do not email the instructors or TAs individually.
• Submit anonymous feedback here.