COGS 188

Instructor: Jason Fleischer

Quarters: Spring 2024

This course gives students a basic understanding of reinforcement learning and AI algorithms. Some of the algorithms discussed include: Breadth-First Search (BFS), Depth-First Search (DFS), Bidirectional Search, A* Search, Simulated Annealing, Dynamic Programming, Policy Iteration, Value Iteration, Monte Carlo Methods, SARSA, Q-Learning, and Eligibility Traces. I designed many of the programming assignments for this course, which include implementing these algorithms in Python and using them to solve real-world problems. The course ends in a final project where students come up with their own problem and experiment with different algorithms on datasets of their choice.

This GitHub repository contains all the programming assignments that were created for this course.