Machine learning is the study and design of algorithms, which enable computers/machines to learn from data. This course is an introduction to machine learning. It provides a broad view of models and algorithms, discusses their methodological foundations, as well as issues of practical implementation, use, and techniques for assessing the performance.
At the end of the course the students will (1) understand and implement common machine learning methods, (2) recognize the problems that are amenable to machine learning, and perform appropriate data analysis, and (3) recognize failure points and threats to validity of the results.
Required texts: (JW) An Introduction to Statistical Learning. G. James, D. Witten, T. Hastie, R. Tibshirani, Springer 2013. (HT) Elements of Statistical Learning. T. Hastie, R. Tibshirani and J. Friedman, Springer, 2009. (CB) Pattern Recognition and Machine Learning. C. M. Bishop, Springer 2006.
Optional texts: (Murphy) Machine Learning: A Probabilistic Perspective. Kevin P. Murphy, MIT Press 2012. (Duda) Pattern Classification. R. O. Duda, P. E. Hart, D. Stork, Wiley and Sons, 2001. (Mitchell) Machine Learning. T. Mitchell, McGraw-Hill, 1997. Additional texts are posted dynamically on Piazza