Applied Statistical Learning

Objective

At the end of this course, students should be able to :

- apply the methods seen in class to concrete problems;

- use the R commands to call upon the procedures seen in class;

- provide a statistical analysis of the methods introduced in class;

- consider, in a study framework similar to those seen in class, a new learning method;

- reproduce (in a guided way) the evidence patterns seen in class.

References

• C. Bishop. Pattern Recognition and Machine Learning. Springer 2006. This is an excellent introduction to machine learning. Contains lots of exercises, some with exemplary solutions. 
• R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley & Sons, second edition, 2001. The classic introduction to the field. 
• Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT Press, 2016.
• Mohri, Mehryar, Afshin Rostamizadeh, and Ameet Talwalkar. Foundations of machine learning. MIT press, 2018.
• L. Wasserman. All of Statistics: A Concise Course in Statistical Inference. Springer, 2004. This book is a compact treatment of statistics that facilitates a deeper understanding of machine learning methods. 
• K. Murphy. Machine Learning: A Probabilistic Perspective. MIT, 2012. Unified probabilistic introduction to machine learning. 
• S. Shalev-Shwartz, and S. Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014. This recent book covers the mathematical foundations of machine learning. Available for personal use online: Link.