Teaching | Jeremy McMahan

Teaching Summary

My teaching experience ranges from Course Assistant to Instructor, spanning positions at UIUC, UW-Madison, and the New Horizons Summer School (sponsored by SIGACT and TTIC).

My teaching philosophy centers on breaking down complex topics to simplify learning. My favorite approach to building intuition is through the power of analogy: relating a new, abstract topic to one the students already understand.

I strongly believe in open access to education and am committed to making my course materials publicly available.

Courses Taught

Game Theory, Optimization & Learning
Introduction to Algorithms
Introduction to Artificial Intelligence
Introduction to Data Science
Introduction to Optimization
Algorithms and Models of Computation

Selected Course Materials

Algorithms Lecture Recordings

Complete lecture series from my time as instructor for Introduction to Algorithms at UW-Madison:

Watch Lectures

MARL Theory Notes

Lecture notes and slides from the Multi-Agent RL module of Game Theory, Optimization & Learning at UW-Madison:

Download ZIP

Courses

2025

Guest Lecturer

CS839: Game Theory, Optimization & Learning

Manolis Vlatakis

Spring 2025

Role: Designed and presented a module on the theory of multi-agent reinforcement learning.

Abs Website

Advanced topics of special interest to students in various areas of Computer Science. Each offering of the course will cover a topic selected by the instructor.

2022

Instructor

CS577: Introduction to Algorithms

Jeremy McMahan

Summer 2022

Role: Responsible for curriculum design, lecture delivery, course structure, and student evaluation for a class of 100 students. Designed original lecture slides, lecture notes, lecture videos, and problem sets focusing on the design of efficient algorithms and rigorous proofs of correctness. Managed one TA and grader.

Instructor Award Abs Video Website

Received the UW CS Graduate Instructor Award for excellence in performance as the primary instructor of CS 577.

Basic paradigms for the design and analysis of efficient algorithms: greed, divide-and-conquer, dynamic programming, reductions, graph techniques, and the use of randomness. Computational intractability including typical NP-complete problems and ways to deal with them.
Guest Lecturer

New Horizons in Theoretical Computer Science Summer School

Nicole Immorlica, Yael Kalai, Jelani Nelson, Ashia Wilson, and Antonio Blanca

Summer 2022

Role: Designed and presented lectures spanning complexity theory basics, interactive proofs, and Markov-chain Monte Carlo methods.

Abs Website

New horizons in theoretical computer science is a week-long online summer school which will expose undergraduates to exciting research areas in the area of theoretical computer science and its applications. The school will contain several mini-courses from top researchers in the field. The course is free of charge,and we welcome applications from undergraduates majoring in computer science or related fields. We particularly encourage applications from students that are members of groups that are currently under-represented in theoretical computer science.
Head TA

CS 540: Introduction to Artificial Intelligence

Jerry Zhu, Sharon Li, and Ilias Diakonikolas

Spring 2022

Role: Coordinated the administrative and grading workflow for this large-scale course with 500 students, 8 TAs, and over 20 graders and peer mentors. Developed standardized grading rubrics and course structures in addition to the standard teaching assistant duties.

Teaching Award Abs Website

Received the Golden Brick Award for excellence in teaching. This reward is decided by student and instructor nominations.

Principles of knowledge-based search techniques, automatic deduction, knowledge representation using predicate logic, machine learning, probabilistic reasoning. Applications in tasks such as problem solving, data mining, game playing, natural language understanding, computer vision, speech recognition, and robotics.

2021

TA

CS 540: Introduction to Artificial Intelligence

Josiah Hanna

Fall 2021

Abs Website

Principles of knowledge-based search techniques, automatic deduction, knowledge representation using predicate logic, machine learning, probabilistic reasoning. Applications in tasks such as problem solving, data mining, game playing, natural language understanding, computer vision, speech recognition, and robotics.
Guest Lecturer

New Horizons in Theoretical Computer Science Summer School

Nicole Immorlica, Yael Kalai, Jelani Nelson, Ashia Wilson, and Antonio Blanca

Summer 2021

Role: Designed and presented lectures spanning complexity theory basics, interactive proofs, and Markov-chain Monte Carlo methods.

Abs Website

New horizons in theoretical computer science is a week-long online summer school which will expose undergraduates to exciting research areas in the area of theoretical computer science and its applications. The school will contain several mini-courses from top researchers in the field. The course is free of charge,and we welcome applications from undergraduates majoring in computer science or related fields. We particularly encourage applications from students that are members of groups that are currently under-represented in theoretical computer science.
Head TA

CS 540: Introduction to Artificial Intelligence

Sharon Li and Fred Sala

Spring 2021

Role: Coordinated the administrative and grading workflow for this large-scale course with 500 students, 8 TAs, and over 20 graders and peer mentors. Developed standardized grading rubrics and course structures in addition to the standard teaching assistant duties.

Abs Website

Principles of knowledge-based search techniques, automatic deduction, knowledge representation using predicate logic, machine learning, probabilistic reasoning. Applications in tasks such as problem solving, data mining, game playing, natural language understanding, computer vision, speech recognition, and robotics.

2020

TA

CS 540: Introduction to Artificial Intelligence

Yingyu Liang, Anthony Gitter, Yin Li, and Daifeng Wang

Fall 2020

Abs Website

Principles of knowledge-based search techniques, automatic deduction, knowledge representation using predicate logic, machine learning, probabilistic reasoning. Applications in tasks such as problem solving, data mining, game playing, natural language understanding, computer vision, speech recognition, and robotics.
TA

CS577: Introduction to Algorithms

Shuchi Chawla and Christos Tzamos

Spring 2020

Role: Designed supplementary lecture and guided-problem solving videos to aid in the mastery of key topics, in addition to usual responsibilities.

Teaching Award Abs Video Website

Received the Golden Brick Award for excellence in teaching. This reward is decided by student and instructor nominations.

Basic paradigms for the design and analysis of efficient algorithms: greed, divide-and-conquer, dynamic programming, reductions, and the use of randomness. Computational intractability including typical NP-complete problems and ways to deal with them.

2019

TA

CS577: Introduction to Algorithms

Jiazhen Zhou

Summer 2019

Abs Website

Designing an efficient algorithm for some purpose is a creative activity akin to solving a puzzle or proving a mathematical theorem. We will focus on this design process, and also survey the mathematical tools that can be used to evaluate the efficiency of algorithms. Examples will be drawn from a variety of topics: sorting and searching, algebra, graph theory, geometry, network routing, etc. Aside from their inherent interest, these examples form the basic “toolkit” of practical computing. At the end of the course, we will study inherent limits to the efficiency of algorithms. In particular, we will take up the question of whether there are computational problems that are inherently intractable.
TA

CS 301: Introduction to Data Science

Tyler Caraza-Harter

Fall 2019

Role: Designed supplementary lecture videos to aid in the mastery of key topics, in addition to usual responsibilities.

Teaching Award Abs Video Website

Received the Golden Brick Award for excellence in teaching. This reward is decided by student and instructor nominations.

Introduction to Data Science programming using Python. No previous programming experience required. Emphasis on analyzing real datasets in a variety of forms and visual communication.
TA

CS577: Introduction to Algorithms

Dieter Melkebeek

Spring 2019

Teaching Award Abs

Received the Golden Brick Award for excellence in teaching. This reward is decided by student and instructor nominations.

This course provides an undergraduate-level introduction to the design and analysis of algorithms. The main focus lies on techniques for constructing correct efficient algorithms and on tools to reason about them. Design paradigms include greed, divide-and-conquer, dynamic programming, reductions, and randomness (time permitting). A second focus point is computational intractability. NP-complete problems are covered, as well as ways to deal with them. The course forms a foundation for all areas of computer science. The particular computational problems discussed have applications in artificial intelligence, computational biology, compiler construction, hardware and network protocols, and optimization.

2018

TA

CS 524: Introduction to Optimization

Steve Wright

Fall 2018

Abs Website

Introduction to mathematical optimization from a modeling and solution perspective. Formulation of applications as discrete and continuous optimization problems and equilibrium models. Survey and appropriate usage of basic algorithms, data and software tools, including modeling languages and subroutine libraries.
CA

CS 374: Algorithms and Models of Computation

Jeff Erickson

Spring 2018

Abs Website

CS/ECE 374 covers fundamental tools and techniques from theoretical computer science, including design and analysis of algorithms, formal languages and automata, computability, and complexity. Specific topics include regular and context-free languages, finite-state automata, recursive algorithms (including divide and conquer, backtracking, dynamic programming, and greedy algorithms), fundamental graph algorithms (including depth- and breadth-first search, topological sorting, minimum spanning trees, and shortest paths), undecidability, and NP-completeness. The course also has a strong focus on clear technical communication.