Unit 1: Supervised Learning
- Regression
- Classification
- Basic methods: Distance-based methods, Nearest-Neighbours, Decision Trees, Naive Bayes
- Linear models: Linear Regression, Logistic Regression, Generalized Linear Models
- Support Vector Machines (SVM)
- Nonlinearity and Kernel Methods
- Beyond Binary Classification: Multi-class, Structured Outputs, Ranking
Unit 2: Unsupervised Learning
- Clustering: K-means, Kernel K-means
- Dimensionality Reduction: PCA (Principal Component Analysis), Kernel PCA
- Matrix Factorization and Matrix Completion
- Generative Models: Mixture models and Latent Factor Models
Unit 3: Evaluating ML Algorithms & Model Selection
- Evaluation of Machine Learning Algorithms
- Model Selection
- Introduction to Statistical Learning Theory
- Ensemble Methods: Boosting, Bagging, Random Forests
Unit 4: Advanced Modeling
- Sparse Modeling and Estimation
- Modeling Sequence/Time-Series Data
- Deep Learning and Feature Representation Learning
Unit 5: Scalable and Advanced ML Topics
- Scalable Machine Learning
- Online Learning
- Distributed Learning
- Advanced Topics (selection among):
- Semi-supervised Learning
- Active Learning
- Reinforcement Learning
- Inference in Graphical Models
- Introduction to Bayesian Learning and Inference
Unit 6: Recent Trends
- Recent trends in machine learning learning techniques
- Recent trends in classification methods