This repository is a personal space for consulting notes and projects created during Harvard CS50 courses.
⚠️ If you are consulting these as a CS50 student, keep in mind Harvard's Academy Honesty Policy
https://cs50.harvard.edu/python/2022/ (Completed in July 2023)
FINAL PROJECT: Dutchionary is a Flask web application powered by OpenAI API. I submitted a simple application running in the terminal for the final assignment.
https://cs50.harvard.edu/ai/2020/ (Currently enrolled)
Topics covered: Depth-first search, breadth-first search, uninformed search, informed search, greedy best-first search, A* search, adversarial search, minimax, alpha-beta pruning, depth-limited minimax
PROJECT 01: DEGREES Write a program determining how many “degrees of separation” apart two actors are.
PROJECT 02: TIC-TAC-TOE
Implement an AI to play Tic-Tac-Toe optimally (Minimax).
Topics covered: Propositional logic, inference algorithms, de Morgan’s law, distributive property, inference by resolution
PROJECT 01: KNIGHTS
Write a program to solve logic puzzles.
PROJECT 02: MINESWEEPER
Write an AI to play Minesweeper (Propositional logic, knowledge representation).
Topics covered: Propositional logic, inference algorithms, Bayes' rule, de Morgan’s law, distributive property, inference by resolution
PROJECT 01: PAGERANK
Write an AI to rank web pages by importance (Markov Chain).
PROJECT 02: HEREDITY Write an AI to assess the likelihood of a person having a particular genetic trait (Bayesian network).
Topics covered: Problem formulation (local search, linear programming algorithms, constraint satisfaction), hill climbing (and variants), simulated annealing, backtracking search, inference
PROJECT 01: CROSSWORD Write an AI to generate crossword puzzles (backtrack search).
Topics covered: Supervised learning, reinforcement learning (Markov chain, Q-learning, greedy decision making), unsupervised learning (clustering) | Libraries: SkLearn
PROJECT 01: SHOPPING Write an AI to predict whether online shopping customers will complete a purchase (K-neighbours classifier). Using sklearn.
PROJECT 02: NIM Write an AI that teaches itself to play Nim through reinforcement learning.
Topics covered: Gradient descent (stochastic, mini-batch), deep neural networks, computer vision (image convolution, pooling) Libraries: TensorFlow, MatPlotLib
PROJECT 01: TRAFFIC Write an AI to identify which traffic sign appears in a photograph, using TensorFlow.
Topics covered: Natural language processing, semantics x syntax, n-grams, tokenisation, Markov models, text categorisation, information retrieval, topic modelling | Libraries: Nltk
PROJECT 01: PARSER Write an AI to parse sentences and extract noun phrases.
PROJECT 02: QUESTIONS
Write an AI to ask questions (information retrieval based on a corpus).
