An overview of the techniques that are transforming many industries and will change our lives.
Introduction to Artificial Intelligence series
This MOOC is one of the MOOCs of the series titled “Introduction to Artificial Intelligence”, aimed at providing technical and non-technical, including historical and political, notions on artificial intelligence. The series investigates why artificial intelligence is nowadays considered the most disruptive enabling technologies up to at least 2050 and gives basic groundings for a preliminary approach to the area. It also deepens ethical issues and national strategies.
If you are a PoliMI student or staff member you have to log in using your Person Code. This is the only way to prove your participation in this course for official recognition.
The MOOC provides a general overview of the main methods in the machine learning field. Starting from a taxonomy of the different problems that can be solved through machine learning techniques, the MOOC briefly presents some algorithmic solutions, highlighting when they can be successful, but also their limitations. These concepts will be explained through examples and case studies.
Total workload of the course: 8 hours
This MOOC is provided by Politecnico di Milano.
Information about fees and access to materials
You can access the course absolutely free of charge and completely online.
Course materials will remain available to all enrolled users after the end of the current edition, so they can return to content later. The current course edition will be followed by a new one just after its end.
The course is organized in 3 weeks.
- Week 1 – Supervised Learning
- Week 2 – Unsupervised Learning
- Week 3 – Reinforcement Learning
In particular, Week 1 introduces the main techniques for dealing with supervised learning problems, that are classification and regression. Week 2 explores unsupervised learning techniques for clustering, dimensionality reduction and association rules mining. Finally, Week 3 introduces reinforcement learning for solving sequential decision-making problems.
Intended Learning Outcomes
By actively participating in this MOOC, you will achieve different intended learning outcomes (ILOs).
- Classify machine learning problems
ESCO: utilize machine learning
- Classify supervised learning problems
- Describe the limitations of machine learning techniques in supervised learning
ESCO: principles of artificial intelligence
- Identify the key elements of supervised learning algorithms
- Perform model evaluation and selection in supervised learning
- Classify machine learning problems in unsupervised learning
- Describe the utility of dimensionality reduction techniques
- Describe the main techniques for identifying clusters of data
- Formulate a sequential decision-making problem
- Explain what a value function is and how it can be estimated using reinforcement learning
- Describe how to optimize a policy in reinforcement learning
No prerequisites are required: however, having basic statistical notions may help you better understand some considerations.
Over and above consulting the content, in the form of videos and other web-based resources, you will have the opportunity to discuss course topics and to share ideas with your peers in the Forum of this MOOC.
The final grade for the course is based on results from your responses to the quizzes you will find at the end of each week (weekly quizzes). You will successfully complete the course if you reach 60% (or more) of the total score by the end of the edition. The course’s total score will be calculated by averaging the scores of the assessed quizzes for each individual week.
Certificate of Accomplishment
The Certificate of Accomplishment will be released to anyone who successfully completed the course by achieving at least 60% of the total score in the assessed quizzes. You will be able to download the Certificate of Accomplishment directly from the website.
Once you have successfully passed the course, you can request the Certificate of Accomplishment without waiting for the end of the edition.
The Certificate of Accomplishment does not confer any academic credit, grade or degree.
European Qualifications Framework Level
EQF Level 6
Thematic area (ISCED-F classification)
This MOOC belongs to the following thematic areas:
- 061 Information and Communication Technologies (ICTs)
- 0619 Information and Communication Technologies not elsewhere classified
- 071 Engineering and engineering trades
- 0714 Electronics and automation
The forum of this MOOC is freely accessible and participation is not guided; you can use it to compare yourself with other participants, or to discuss course contents with them.
If you have any enquiries about the course or if you need technical assistance please contact firstname.lastname@example.org. For further information, see FAQ page.
Marcello Restelli is Associate Professor of Computer Engineering at the Dipartimento di Elettronica, Informazione e Bioingegneria of the Politecnico di Milano. where he obtained the Laurea degree in Computer Science Engineering in 2000 and the Ph.D. in Information Engineering in 2004. He is currently teaching the “Machine Learning” and the “Reinforcement Learning” courses and he is a board member of the national Ph.D. programme in Artificial Intelligence - Industry 4.0. His research interests focus on machine learning algorithms and, in particular, the development of reinforcement learning techniques and their application to real-world problems (e.g., robotics, finance, autonomous vehicles, water resource management, etc.).
He has published more than 150 peer-reviewed papers on some of the most prestigious international conferences and journals in the machine-learning and robotic fields. He has served as reviewer for several international journals and he has been member of the programme committee of the main international conference of his research area, among which ICML, NIPS, AAAI, and IJCAI. He is principal investigator of several research projects funded both by public entities and by some of the main Italian companies.
This course is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License except where otherwise specified.