LEARNING OUTCOMES

Students will be provided with advanced skills related to machine learning and data analysis.
Students will learn insights on machine learning and data analysis methodologies and a series of real world applications.

AIMS AND LEARNING OUTCOMES

The student will be able to apply the acquired skills to a case study by deriving the model of the phenomenon that generated the data under analysis.

PREREQUISITES

Coding (Matlab/Python/R), linear algebra, probability and statistics.

TEACHING METHODS

The course consists of lectures and practical lab sessions using Matlab/Python/R

SYLLABUS/CONTENT

1. Statistical inference
2. Supervised, Semisupervised, and Unsupervised Learning
3. Statistical Learning Theory
4. Shallow Machine Learning Algorithms (examples of coding in Matlab/Python/R)
5. Deep Machine Learning Algorithms (examples of coding in Matlab/Python/R)
6. Model Selection and Error Estimation

RECOMMENDED READING/BIBLIOGRAPHY

C. C. Aggarwal "Data Mining - The textbook" 2015
T. Hastie, R.Tibshirani, J.Friedman "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" 2009.
S. Shalev-Shwartz, S. Ben-David "Understanding machine learning: From theory to algorithms" 2014
I. Goodfellow, Y. Bengio, A. Courville "Deep learning" 2016
L. Oneto "Model Selection and Error Estimation in a Nutshell" 2020