These are projects of the 23/24 A.Y. second semester:
Harmful Brain Activity Classification: Detect and classify seizures and other types of harmful brain activity. You will develop a model trained on electroencephalography (EEG) signals recorded from critically ill hospital patients.
Digit Recognizer: Correctly identify digits from a dataset of tens of thousands of handwritten images.
Connect X: In this game, your objective is to get a certain number of your checkers in a row horizontally, vertically, or diagonally on the game board before your opponent. When it's your turn, you “drop” one of your checkers into one of the columns at the top of the board. Then, let your opponent take their turn. This means each move may be trying to either win for you, or trying to stop your opponent from winning.
Reinforcement learning: Use a double dueling DQN to train an agent that can achieve a superhuman level at the famous Atari Breakout game ("ALE/Breakout-v5"). The observations are images. To simplify the task, you should convert them to grayscale (i.e., average over the channels axis) then crop them and downsample them, so they’re just large enough to play, but not more. An individual image does not tell you which way the ball and the paddles are going, so you should merge two or three consecutive images to form each state. Lastly, the DQN should be composed mostly of convolutional layers.
Recommendation algorithms: using Movielens 10m data set, our goal is to predict the rating an user would give to a movie she haven’t watched yet. We’ll explore matrix factorization algorithms and some computational statistical methods such as GLMMs. Furthermore, we’ll use Bayesian methods for the matrix factorization and will implement hierarchical Poisson factorization using nonparametric Bayesian methods.
Urban Housing Price Cluster Analysis: This project focuses on analyzing housing price data across city neighborhoods or entire countries to identify and understand price clusters. By examining historical data, we aim to track how different areas have evolved in terms of affordability, highlighting trends in urban development and economic shifts. This streamlined analysis offers insights into the changing dynamics of real estate values over time.
NOTE: You are free to apply to any of these projects, as well as the ongoing Scavenger AI project and Applying Convolutional Neural Networks (CNNs) to Electrical Circuit Analysis.