OVERVIEW

The course aims at introducing the learner to the theory and applications of machine learning, particularly deep learning.

AIMS AND CONTENT

LEARNING OUTCOMES

The course aims at providing the learner with state-of-the-art knowledge, both in terms of algorithms/models and tools, to tackle problems using machine learning techniques.

AIMS AND LEARNING OUTCOMES

The main objective of the course is for the student to come to possess a broad knowledge of state-of-the-art deep learning techniques (dense, convolutional, recurrent networks). For each topic covered, the student will have the opportunity to learn the theoretical foundations, and to study some application examples. Exercises are proposed, and usually solved in class, for each topic in order to stimulate application and test knowledge acquisition. The examples and exercises in the course will use the python language and the Keras/Tensorflow library.

The learning outcomes relate to the realization of the above learning objectives, including through the analysis of application cases.

PREREQUISITES

Fundamentals of programming (particularly python).

A series of seminars on programming will be offered initially so that everyone can take the course on a regular basis

TEACHING METHODS

Lectures face-to-face, using slides, and examples/exercises carried out on the PC (or in tele-learning, if made necessary), mainly using the Keras/Tensorflow library, in python language. Student Reception. Proposal, implementation and discussion of a project.

SYLLABUS/CONTENT

Machine learning

• Introduction to machine learning
• Linear regression
• Gradiet descent
• Classification
• Training
• Regularization
• Multilayer perceptron
• Training deep neural networks
• Pre-training and fine tuning
• Convolutional neural network architectures
• Object detectors
• Processing of sequences
• Recurrent neural networks
• Unsupervised machine learning (clustering)
• Genetic algorithms

RECOMMENDED READING/BIBLIOGRAPHY

A. Geron, Hands-On Machine Learning With Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent, O’ Reilly

I. Goodfellow, Y. Bengio and A. Courville, Deep Learning, The MIT Press

Lecture notes and other material suggested by the lecturer during the course

TEACHERS AND EXAM BOARD

Exam Board

FRANCESCO BELLOTTI (President)

ALBERTO CABRI (President Substitute)

LESSONS

LESSONS START

https://easyacademy.unige.it  

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Written and/or oral examination on topics covered in class

ASSESSMENT METHODS

Verification of the knowledge acquired and the ability to apply it in contexts other than those presented in class will be assessed through questions in the interview or written examination.

The evaluation will also take into account the student's participation during the course.

Exam schedule