The student can opt to a machine learning project on themes proposed by the professor or of interest of the student and validated by the professor.
The student will have to:
know the fundamental concepts and algorithms for the automatic learning by means of digital devices.
know how to acquire and process data sets for learning purposes. Know to use the MATLAB programming language.
Know how to analyse an automatic learning problem both in supervised and unsupervised fashion. Indicate the expected education results as described in the following Dublin descriptors.
Knowledge and comprehension
Acquire specific knowledge of the main concepts and theory of the automatic learning by means of digital devices. T
o know and know how to use the MATLAB programming language for the implementation of machine learning algorithms.
Capacity to apply knowledge and comprehension
-Know to analyse and comprehend a machine learning algorithm.
-Know to analyse and interpret a machine learning problem and to apply the aforementioned knowledge to select the best learning scheme.
-Design an automatic learning system for sampled data. Judgment autonomy
-Know to evaluate the machine learning algorithms and to provide a personal choice of the most proper algorithm to solve a given problem.
-Know to distinguish between different solutions of machine learning and evaluate their performance.
-Know to present, written and orally, with rigorous logic and terminology technical issues related to algorithms and systems for the automatic learning by means of digital devices. Comprehension capabilities
-Know how to retrieve and use bibliographic and digital instruments useful for the personal investigation of problems related to automatic learning.
1.Introduction – Presentation of the course and of the basic concepts of machine learning like vectors and features spaces. Machine learning applications like classification, regression, clustering and data compression. Classification of the machine learning techniques in supervised or unsupervised learning.
2.Regression – Linear regression techniques (minimization of the cost function by means of gradient descent techniques, least square, polynomial regression). Logistic regression both binomial or polynomial.
3.Supervised algorithms – Introduction to the classification algorithms. Most known algorithms (Decision trees, neural networks, deep learning, convolutional neural networks CNN, autoencoders, GAN, transformers, kernel, support vector machines, reinforcement learning, explainable AI). Back propagation algorithm.
4.Non supervised algorithms – Clustering definition and main clustering techniques (k-means, GMM, EM, etc.) . Data compression techniques by means of dimensionality reduction techniques like PCA and LDA.
5.Applications – The use of machine learning algorithm for real purposes like perception, text analyses, artificial vision, medicine, economy and others.
6.Laboratory – Implementation of most known regression, classification and reinforcement learning algorithms. The language used will be python with pytorch modules.
 Christopher M . Bishop, Pattern Recognition and Machine Learning, Information Science and Statistics, 2006.