Course Description

Objective

The main goal of Machine Learning (ML) is the development of systems that are able to autonomously change their behavior based on experience. ML offers some of the more effective techniques for knowledge discovery in large data sets. ML has played a fundamental role in areas such as bioinformatics, information retrieval, business intelligence and autonomous vehicle development.

The main goal of this course is to study the computational, mathematical and statistical foundations of ML, which are essential for the theoretical analysis of existing learning algorithms, the development of new algorithms and the well-founded application of ML to solve real-world problems.

Methodology

    • Professor's lectures on fundamental topics

    • Practical assignments and exercises to be solved by students

    • Technical papers' review and presentation by students

    • Final project

    • Written and practical tests. Students must show a good grasp of concepts and skills covered in the course.

Contents

Grading

    • Assignments 40%

    • Exams 30%

    • Presentation 15%

    • Final project 15%

Grades page

References

    • [Alp10] Alpaydin, E. 2010 Introduction to Machine Learning, 2Ed. The MIT Press.

    • [Bis06] Bishop, C. 2006 Pattern Recognition and Machine Learning. Springer-Verlag.

    • [Mit97] Mitchell, T. M. 1997 Machine Learning. 1st. McGraw-Hill Higher Education.

    • [DHS00] Duda, R. O., Hart, P. E., and Stork, D. G. 2000 Pattern Classification (2nd Edition). Wiley-Interscience.

    • [HTF01] Hastie, T. and Tibshirani, R. and Friedman. 2001 The elements of statistical learning: data mining, inference, and prediction. Springer.

    • [SC04] Shawe-Taylor, J. and Cristianini, N. 2004 Kernel Methods for Pattern Analysis. Cambridge University Press.

    • [TSK05] Pang-Ning Tan, Michael Steinbach, Vipin Kumar, 2005, Introduction to Data Mining, Addison-Wesley.

    • [CST00] Cristianini, N. and Shawe-Taylor, J., 2000, An introduction to support Vector Machines: and other kernel-based learning methods,, Cambridge Univ Press.

    • [SS02] Scholkopf, B. and Smola, A.J., 2002, Learning with kernels, MIT Press.

    • [Bak07] Bakir, G. (Ed), 2007, Predicting Structured Data, MIT Press.

    • [OCW-ML] 6.867 Machine Learning, Fall 2006, MIT OpenCourseWare.

    • [STANFD-ML] Andrew Ng, CS229 Machine Learning, Stanford University