Course 2021-2022 a.y.

• Introduction to the theory of Machine Learning.
• Review of probability tools.
• Statistical inference and regression techniques.
• Unsupervised methods: Principal Component Analysis, hierarchical clustering, k-means.
• Supervised methods: K-nearest neighbours,  Support Vector Machines, Multi Layer Neural Networks.
• Implementing Machine Learning Pipelines with sklearn

• State and use the basic results of statistical inference for data analysis.
• Distinguish between supervised and unsupervised learning methods.
• Describe the  basic conceptual ideas, strengths and the limitations of the different learaning algorithms.

• Implement a complete data analysis pipeline, from unsupervised clustering of data to supervised classification using the Python scikit-learn library.
• Demostrate the ability to cope with real-world data analysis and problem solving (managing, preprocessing and analyze real datasets).

• Face-to-face lectures
• Individual assignments

• Individual assignemnts: each student is required to solve a machine learning problem and provide a written report and a code.

The exam consists of a theory part and problem solving projects.

• The theory part consists in exercises and questions to be answered on paper, and is used to asses the "knowledge and understanding" learning   objectives. This contributes to 50% of the final grade.
• The project consists in a programming code to solve a concrete ML problem, to be developed individually and described through a written report, which is evaluated by the teachers. This contributes to 50% of the final grade.

The individual project is used to asses the "applying knowledge and understanding" learning objectives. In order to pass the exam, students must achieve a passing grade  in both the theory part and the project part.

• D. MACKAY, Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2013 (but available for download as decided by the Author).
• C.M. BISHOP, Pattern Recognition and Machine Learning, Springer, 2006.
• T. HASTIE, R. TIBSHIRANI, J. FRIEDMAN, The Elements of Statistical Learning, Springer, 2009. 
• I. GOODFELLOW, Y. BENGIO, A. COURVILLE,  Deep Learning, MIT Press, 2016 (optional).
• Handouts of each lecture and sample codes are provided.