Supervised Learning (Regression/Classification): Basic methods: Distance-based methods, Nearest-Neighbors,

Decision Trees, Naı̈ve Bayes. Linear models: Linear Regression, Logistic Regression, Generalized Linear

Models. Support Vector Machines, Nonlinearity and Kernel Methods. Beyond Binary Classification:

Multi-class/Structured Outputs, Ranking

Unsupervised Learning: Clustering: K-means/Kernel K-means. Dimensionality Reduction: PCA and kernel PCA. Matrix Factorization and Matrix Completion. Generative Models (mixture models and latent factor models)

Assorted Topics: Evaluating Machine Learning algorithms and Model Selection. Introduction to Statistical

Learning Theory. Ensemble Methods (Boosting, Bagging, Random Forests). Sparse Modelingand Estimation. Modeling Sequence/Time-Series Data. Deep Learning and Feature Representation Learning. Scalable Machine Learning (Online and Distributed Learning). A selection from some other advanced topics,e.g., Semi-supervised Learning, Active Learning, Reinforcement Learning, Inference in Graphical Models,

Introduction to Bayesian Learning and Inference.

1. Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012
2. Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning, Springer 2009.
3. Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2007.
4. Hal Daumé III, A Course in Machine Learning, 2015.
5. Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman, Data Science and Machine Learning, Mathematical and Statistical Methods, CRC Press, 2019.
6. Ethem Alpaydin, Introduction to Machine Learning, MIT Press, 2014.