Machine Learning Bio
The following are last-minute news you should be aware of ;-)
* 24/02/2021: Lectures start today
Course Aim & Organization
The objective of the Machine Learning course is to give an in-depth presentation of the techniques most used for pattern recognition, knowledge discovery, and data analysis/modeling. These techniques are presented both from a theoretical (i.e., statistics and information theory) perspective and a practical one (i.e., coding examples) through the descriptions of algorithms and their implementations in a general-purpose programming language (i.e., python).
The course presents the classical supervised and unsupervised learning paradigms described and discussed presenting regression, classification, and clustering problems in Bioinformatics. The course is composed of a set of lectures on specific machine learning techniques (e.g., generalized linear regression, logistic regression, linear and quadratic discriminant analysis, support vector machines, k-nearest-neighborhood, clustering, etc.) preceded by the introduction of the Statistical Learning framework which acts as a common reference framework for the entire course.
The course is composed of a blending of lectures and exercises by the course teacher and a teaching assistant.
- Matteo Matteucci: the course teacher and here it is his webex room
- Marco Cannici: the teaching assistant and here it is his webex room
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 lectures will present the theory and practice of the following:
- Machine Learning and Pattern Classification: the general concepts of Machine Learning and Pattern Recognition are introduced within the framework of Statistical Decision Theory with reference to the bias-variance trade-off and the Bayes classifier;
- Generalized Linear Regression: linear methods for regression will be presented and discussed introducing different techniques (e.g., Linear Regression, Ridge Regression, K-Nearest Neighbors Regression, Non-Linear Regression, etc.) and the most common methodologies for model validation and selection (e.g., AIC, BIC, cross-validation, stepwise feature selection, Lasso, etc.).
- Linear and Non-Linear Classification: generative and discriminative techniques for classification will be described and discussed (e.g., Logistic Regression, Linear and Quadratic Discriminant Analysis, K-Nearest Neighbors, Perceptron Rule, and Support Vector Machines, etc.). Metrics for classifiers evaluation and comparison are presented in this part of the course (e.g., accuracy, precision, recall, ROC, AUC, F-measure, Matthew coefficient).
- Unsupervised Learning: the most common approaches to unsupervised learning are described mostly focusing on clustering methods such as hierarchical clustering, k-means, k-medoids, Mixture of Gaussians, DBSCAN, etc
These topics will be presented both from a theoretical perspective and a practical one via implementations in the general-purpose programming language python.
Detailed course schedule
A detailed schedule of the course can be found here; topics are just indicative while days and teachers are correct up to some last-minute change (I will notify you by email). Please note that not all days we have lectures!!
Note: Lecture timetable interpretation * On Wednesday, in room B.6.1, starts at 14:15 (cum tempore), ends at 18:15 * On Thursday, in room B.6.1, starts at 08:15 (cum tempore), ends at 10:15
|Date||Day||Time||Room||Teacher||Type||Topic||24/02/2021||Wednesday||15:15-19:15||B.6.1||Matteo Matteucci||Lecture||Course Intro + Machine Learning Intro|
|25/02/2021||Thursday||08:15-10:15||B.6.1||Matteo Matteucci||Lecture||Statistical Learning Theory|
|03/03/2021||Wednesday||15:15-19:15||B.6.1||Matteo Matteucci||Lecture||Statistical Learning Theory|
|04/03/2021||Thursday||08:15-10:15||B.6.1||Matteo Matteucci||Lecture||Introduction to Linear Algebra|
|10/03/2021||Wednesday||15:15-19:15||B.6.1||Marco Cannici||Python Laboratory||Python + Numpy + Bias/Variance|
|11/03/2021||Thursday||08:15-10:15||B.6.1||Matteo Matteucci||Lecture||Simple Linear Regression|
|17/03/2021||Wednesday||15:15-19:15||B.6.1||Matteo Matteucci||Lecture||Multi Variate Linear Regression|
|18/03/2021||Thursday||08:15-10:15||B.6.1||Matteo Matteucci||Lecture||Multi Variate Linear Regression|
|24/03/2021||Wednesday||15:15-19:15||B.6.1||Marco Cannici||Python Laboratory||Multivariate Linear Regression Laboratory|
|25/03/2021||Thursday||08:15-10:15||B.6.1||Matteo Matteucci||Lecture||Generalized Linear Regression|
|31/03/2021||Wednesday||15:15-19:15||B.6.1||Matteo Matteucci||Lecture||Feature Selection|
|07/04/2021||Wednesday||15:15-19:15||B.6.1||Marco Cannici||Python Laboratory||Generalized Linear Regression and Feature Selection Laboratory|
|08/04/2021||Thursday||08:15-10:15||B.6.1||Matteo Matteucci||Lecture||Classification: KNN and Logistic Regression|
|14/04/2021||Wednesday||15:15-19:15||B.6.1||Matteo Matteucci||Lecture||Classification: Logistic Regression|
|15/04/2021||Thursday||08:15-10:15||B.6.1||Matteo Matteucci||Lecture||Classification: Linear Discriminanr Analysis|
|22/04/2021||Wednesday||15:15-19:15||---||---||---||--- No Lecture ---|
|23/04/2021||Thursday||08:15-10:15||---||---||---||--- No Lecture ---|
|28/04/2021||Wednesday||15:15-19:15||---||---||---||--- No Lecture ---|
|29/04/2021||Thursday||08:15-10:15||B.6.1||Matteo Matteucci||Lecture||Evaluation methods for classification|
|05/05/2021||Wednesday||15:15-19:15||B.6.1||Marco Cannici||Python Laboratory||Logistic Regression and LDA Laboratory|
|06/05/2021||Thursday||08:15-10:15||B.6.1||Matteo Matteucci||Lecture||Evaluation methods for classification|