Course Syllabus
Aims
The aim of the course is to develop the skills for solving supervised learning problems.
Contents
In this course we will introduce and develop different machine learning algorithms for different types of data (images, videos, signals, texts, etc.) in different domains.
Detailed program
In detail the topics addressed are:
- Machine learning algorithms adopted in this course: pros and cons
- Classification and regression frameworks: experiment definition, dataset split, metrics, augmentation, etc.
- Machine learning algorithms design and evaluation for classification and recognition tasks in the image domain such as consumer photos, fashion, medical images, etc.: image classification, image captioning, object detection, face recognition, image segmentation, quality control, etc.
- Machine learning algorithms design and evaluation for classification and recognition tasks in the signal domain such as audio, ecg: identity recognition, activity recognition, etc.
- Machine learning algorithms design and evaluation for regression tasks in different domains and applications such as: quality assessment, forecast, etc.
Prerequisites
Basic programming skills.
Basic knowledge in statistics and mathematics.
Teaching form
The course will be composed of frontal/theoretical classes concerning the methods and interactive practical classes concerning the case studies and applications using Matlab and/or Python. Lectures will be held in presence, unless further COVID-19 related restrictions are imposed.
Textbook and teaching resource
Slides, articles, and notes given by the professor in addition to the textboox:
- Hastie T., Tibshirani R., Friedman J. (2021). The Elements of Statistical Learning (2nd edition). Springer Verlag.
Semester
Second.
Assessment method
The exam consists of a project, where students divided into small groups must design, implement and write a report about the chosen supervised learning task, and its oral presentation during which theoretical contents of the course can also be verified.
Office hours
After the class or agreed by email.
