Assignments
Problem Set 4: Unsupervised Learning
Unsupervised learning: k-means image compression, PCA and autoencoders on MNIST, t-SNE and hierarchical clustering, latent dynamics of a video
Problem Set 3: Neural Network Basics and Project Proposal
Gradients by hand, neural networks in PyTorch on two spirals, a symmetric network, and the project proposal (LaTeX, posted as a PDF on Slack)
Problem Set 2: Logistic Regression and Classification
Logistic regression derivations, classification with scikit-learn, the classification lab, project pre-proposal
Problem Set 1: Linear Regression
Linear regression, Kaggle competition, Learning with LLMs, weather data exploration
Problem Set 0: Data, Math, and Chatting with LLMs
Calculus and linear algebra review. Chatting with LLMs.
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