These are my problem set solutions that I cooked together during my quarter in Summer 2025.

I also attached the compilation of cheatsheets that carried me through the exam, at least that well that I didn't fail, haha!¹

• Linear Regression: Gradient descent, normal equations, regularization
• Logistic Regression: Binary and multiclass classification
• Naive Bayes: Text classification and spam detection
• Support Vector Machines (SVM): Linear and kernel methods
• Decision Trees: Information gain and recursive splitting
• Neural Networks: Backpropagation and deep learning
• Poisson Regression: Count data and exponential family models

• K-Means Clustering: Centroid-based clustering
• Principal Component Analysis (PCA): Dimensionality reduction
• Gaussian Mixture Models (GMM): Probabilistic clustering

• Expectation-Maximization (EM): Learning with partial labels
• Co-training: Multi-view learning approaches

• Q-Learning: Value-based methods
• Policy Optimization: Direct policy search
• Markov Decision Processes (MDPs): Sequential decision making

• Cross-Validation: Model assessment techniques
• Regularization: Ridge, Lasso, and elastic net
• Feature Selection: Forward/backward selection
• Bias-Variance Tradeoff: Model complexity analysis