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 50%
Presentation 20%
Final project 30%
References
[Alp10] Alpaydin, E. 2010 Introduction to Machine Learning, 2Ed. The MIT Press.
[Mur12] Murphy, Kevin P. Machine learning: a probabilistic perspective. The MIT Press, 2012.
[Barber2013] Barber, David, Bayesian Reasoning and Machine Learning, Cambridge University Press, 2013.
[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
Resources
SciPy: scientific, mathematical, and engineering package for Python
scikit-learn: machine learning Scipy add-on
Kaggle: datascience competition, many interesting data sets and different competitions with prizes.
Final project:
Register at Yelp Dataset Challenge
Obtain the data
Think of an interesting problem that may be addressed using the data
Write a proposal