- W1: Introduction to Machine Learning
- W2: Clustering
- W3: Principal Components Analysis
- W4: Recommender Systems
Unsupervised & Representation Learning
Learn methods for uncovering structure in unlabeled data and transforming data into more useful representations for downstream tasks.
Model Selection, Generalization & Optimization
Develop the theoretical and practical tools needed to build reliable models, including feature selection, generalization, and optimization of learning algorithms.
- W5: Feature Selection
- W6: Bias-Variance Tradeoff & Cross Validation
- W7: Basic Optimization
Supervised Learning Models
Study core supervised learning algorithms for predicting outcomes from labeled data, focusing on classification and nonlinear modeling.
- W8: Classification
- W9: Support Vector Machine
- W10: Neural Networks
Advanced Modeling Techniques & Ensemble
Explore methods that improve predictive performance by combining models or addressing specialized prediction tasks such as rare-event detection.
- W11: Anomaly Detection
- W12: Boosting Algorithms
- W13: Decision Trees & Random Forests