Courses

Regression and classification problems

Associated exercises in [1]

  • Ex 1, 4 and 5 Chap 3.7
  • Ex 1 and 2 Chap 4.7

PAC learning theory

Associated exercises in [5]

  • Ex 1, 2 and 3, Chap 3.5
  • Ex 2 and 3, Chapt 6.8

Gradient descent

Computer class in Python, link to the jupyter notebook is given in class.

Convex problem

Slides

Associated exercises in [3]

  • Ex 2.1, 2.8, 2.12 in Chap 2
  • Ex 3.1, 3.17 in Chap 3

Evaluation

  • 75% paper exam
  • 25% homework (computer program)

Suggested Readings

  • [1] James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning . New York: Springer.
  • [2] Hart, P. E., Stork, D. G., & Duda, R. O. (2000). Pattern classification. Hoboken: Wiley.
  • [3] Boyd, S. P., & Vandenberghe, L. (2004). Convex optimization. Cambridge university press.
  • [4] CornuĂ©jols, A., & Miclet, L. (2011). Apprentissage artificiel: concepts et algorithmes. Editions Eyrolles.
  • [5] Shalev-Shwartz, S. and Ben-David, S. (2014), Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press.

Further Readings

  • [6] Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction. New York: Springer.
  • [7] Bertsekas, D. (2009). Convex optimization theory(Vol. 1). Athena Scientific.
  • [8] Bubeck, S. (2015). Convex optimization: Algorithms and complexity. Foundations and Trends in Machine Learning, 8(3-4), 231-357.