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Teaching

For a complete list of my teaching experience, see the home page. Here are some teaching materials I have developed over the years.

CSC236 (Introduction to the Theory of Computation)

I am the current (Summer 2025) course instructor for CSC236. You can find the course webpage here.

Here is the course webpage for when I taught the course in Summer 2023.

Notebooks + Coding Projects

For CSC373 (Algorithms, Design and Analysis)

These notebooks accompany the undergraduate algorithms course at U of T. I developed them when I taught the course in 2024.

• Huffman Coding and Closest Pair in ${\mathbb{R}}^2$
  • In this notebook, students implement two algorithms from class: Huffman Coding and the Closest pair in ${\mathbb{R}}^2$
  • There are visualizations for both algorithms
  • For the closest pair in ${\mathbb{R}}^2$ problem, there is a runtime comparison with the naive algorithm.
  
• Dynamic Programming
  • In this notebook, students implement DP algorithms for the knapsack problem and the traveling salesman problem.
  • Students who completed the Journey through Hoenn notebook in 236 will now be able to find an exact solution (instead of an approximate one)!
  
• Flow and LP
  • In this notebook, students model and solve matchmaking problem - matching CS students with business students for a group project.
  • The unweighted version (maximum cardinality matching) is solved via max flow
  • The weighted version is solved via LP
  • Python libraries networkx and scipy are used to solve max flow and linear programming respectively.
  

For CSC236 (Introduction to the Theory of Computation)

• A Journey Through Hoenn
  • In this notebook, students model the problem of finding the quickest way to visit each city in the City of Hoenn (from Pokemon Emerald) as a graph problem and approximate a solution using the networkx library
  • There are variants for where you can move in any direction and where you can move only up/down/left/right.
  • As a bonus, the notebook explores a relaxation of the problem where backtracking is `free.` I.e. If I have visited a city before, I can fly to it again at zero cost. This variant can then be modeled by another well-known graph problem.
  

For Inspirit AI

These are notebooks I developed for Inspirit AI. These projects are for students at the high school and middle school levels.

• Self driving cars: 1, 2, 3
  • In this project, students gradually build models to drive cars in one of the OpenAI Gyms environments.
  • The project starts with basic models to decide whether the road turns left or right and gradually builds to models based on behavioral cloning and deep Q-learning.
  
• Covid mask detection: 1, 2, 3
  • In this project, students build models to detect whether or not someone is wearing a mask.
  • The models start very simple (using the color distribution of the input image) and gradually get more complex, building up to a convolutional neural network with a facial recognition preprocessing step.
  • Students then get a chance to test the model on their images.
• Game playing AI - the minimax algorithm and variants
  • Students implement the minimax algorithm and use it to play tic-tac-toe and connect 4.
  • Students tune a heuristic evaluation function and then get to play against their algorithm, which is always fun :)