Artificial intelligence · Data science · Computer science

Learning through concepts, code, and experimentation

I teach courses in artificial intelligence, reinforcement learning, data mining, and computer science, with an emphasis on connecting foundational ideas to practical implementations and reproducible experiments.

Current courses — Fall 2026

CS 5/7320 · Lyle School of Engineering

Artificial Intelligence

Course materials, examples, and exercises are available on the course website.

See course material · Canvas course page

CS 5/7329 · Lyle School of Engineering

Reinforcement Learning

Course materials, examples, and exercises are available on the course website.

See course material · Canvas course page

Teaching resources

Open lecture materials, code examples, exercises, and small teaching tools used in my classes.

Artificial Intelligence

Python · Course materials

Slides, compact code examples, and exercises for an introduction to AI using Russell and Norvig’s Artificial Intelligence: A Modern Approach.

Reinforcement Learning

Python · Course materials

Slides, code examples, and exercises for an introductory course in reinforcement learning.

Data Structures

C++ · Code examples

Code for an introductory data-structures course using Mark Allen Weiss’s Data Structures and Algorithm Analysis in C++.

gym-classics2

Python · RL package

Classic discrete finite Markov decision processes and algorithms for teaching reinforcement learning.

Additional examples

R · C++ · Small tools

Tools for fitting distributions and Gridhunt2, a game for teaching encapsulation, composition, inheritance, and polymorphism.

Video lectures

Introduction to R Programming

A video series covering the foundations of programming and data analysis with R.
Here is the lecture material, code examples, and data.

Watch the playlist