Teaching

At Carnegie Mellon University I serve as a teaching assistant for two introductory-level AI courses 15-281 AI Representation and Problem Solving and 07-180 Concepts in AI. The first is the “standard” intro to AI course (similar to CS 188 at UC Berkeley) and the latter is a half-semester mini course designed for students to decide whether they want to major in AI.

In addition to the regular TA responsibilities (e.g., holding office hours, grading assignments), I spend time emphasizing the 1) motivations of different topics, 2) how they are connected to each other, and 3) how they can be extended outside the scope of the course. Personally, I believe having a solid understanding of the fundamental idea behind an algorithm is the most important part of learning, and the appreciation of non-learning methods in AI is crucial yet overlooked in the current era overwhelmed by deep learning.

Among other materials that I have created, I am particularly proud of the design of the Search as IP problem, which aims to solidify the student’s understanding of both search algorithms and integer programming. (But it is unfortunately deemed as too hard for the course).