Technology
Subject: FONDAMENTI DI MACHINE LEARNING (A.A. 2024/2025)
degree course in COMPUTER ENGINEERING
| Course year | 3 |
|---|---|
| CFU | 9 |
| Teaching units |
Unit Fondamenti di Machine Learning
Computer Engineering (lesson)
|
| Mandatory prerequisites |
Inglese |
| Exam type | written |
| Evaluation | final vote |
| Teaching language | Italiano |
Teachers
Overview
Learn the principles and techniques behind machine learning methods. Acquire knowledge of numerical data processing. Design machine learning solutions by combining classification and regression algorithms and applying data pre-processing pipelines.
Admission requirements
Basic knowledge of statistics, linear algebra and mathematical analysis (calculus)
Course contents
Regression (CFU: 3).
- Linear Regression
- Feature selection and regolarization
Classification (CFU: 3):
- Parametric methods
- Non-parametric methods
- Ensemble
Artificial neural networks and unsupervised learning (CFU: 3):
- Clustering
- Artificial neural networks
- Introduction to Deep Learning
Teaching methods
The course is structured in theoretical lectures and laboratory lectures on the Python implementation of the techniques presented. Students will also be presented with real problems to solve with the tools learned.
Assessment methods
Preparation of a project (report and implementation) and its oral discussion with questions on the practical application of techniques and algorithms covered during the course. - Project activity (50% of final grade): Design and Python implementation of data analysis algorithms related to a problem chosen by the student in agreement with the Professor. Production of a project report illustrating the activity. The material (report and implementation) is due on the date of the exam. The workload should be approximately 10 man-days. While conducting the project activity, the student can access all the documentation and material he/she deems necessary. - Oral Examination (50% of final grade): Discussion of the project activity and the topics covered in class.
Learning outcomes
Knowledge and understanding of the main pattern recognition and machine learning techniques for analyzing heterogeneous data.
Ability to apply the main techniques of supervised and unsupervised learning for data analysis.
Autonomy in finding and autonomously studying supplemental material.
Ability to design solutions to interdisciplinary problems and ability to illustrate the proposed solutions.
Readings
- A. Cucci, A tu per tu col Machine Learning. L'incredibile viaggio di un developer nel favoloso mondo della Data Science, The Dot Company, 2017
Approfondimento [Further reading]:
- G. James, D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statistical Learning, Springer, 2013
- T. Hastie, R. Tibshirani, J. Friedman, The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media, 2009.
- C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006
- R. O. Duda, P. E. Hart, d. G, Stork, Pattern Classification - Second Edition. Wiley Interscience, 2000