Machine Learning Bio
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* 03/03/2020: The course is going to start soon ...
Course Aim & Organization
The course is composed by a blending of lectures and exercises by the course teacher and a teaching assistant.
Techniques from machine and statistical learning are presented from a theoretical (i.e., based on statistics and information theory) and practical perspective through the descriptions of algorithms, the theory behind them, their implementation issues, and few examples from real applications. The course mostly follows the following book which is also available for download in pdf
- An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
The course is composed by a set of ex-cattedra lectures on specific techniques (e.g., linear regression, linear discriminant analysis, clustering, etc.). Supervised and unsupervised learning are discussed in the framework of classification and clustering problems. The course outline is:
- Machine Learning and Pattern Classification: the general concepts of Machine Learning and Patter Recognition are introduced with a brief review of Statistical Decision Theory;
- Linear Regression Techniques: linear methods for regression will be disccussed and compared (e.g., Linear Regression and Ridge Regression).
- Linear Classification Techniques: linear methods for classification will be presented as the starting point for more complex methods (e.g., Linera Regression on the indicator matrix, Linear and Quadratic Discriminant Analysis, Logistic Regression, Percptron rule and Optimal Separating Hyperplanes, a.k.a., Support Vector Machines)
- Unsupervised Learning Techniques: the most common approaches to unsupervised learning are described mostly focusing on clustering techniques such as hierarchical clustering, k-means, k-medoids, Mixture of Gaussians, DBSCAN, Jarvis-Patrick, etc.;
- Model Validation and Selection: model validation and selection are orthogonal issues to all previous techniques; during the course their fundamentals are described and discussed in the framework of linear models for regression (e.g., AIC, BIC, cross-validation, etc. ).
Detailed course schedule
The course evaluation is composed by two parts:
- HW: Homework with exercises covering the whole program
- WE: A written examination covering the whole program