Machine Learning
Machine Learning
An overview of the techniques that are transforming many industries and will change our lives.
This MOOC was produced as part of the Edvance project – Digital Education Hub per la Cultura Digitale Avanzata. The project is funded by the European Union – Next Generation EU, Component 1, Investment 3.4 “Didattica e competenze universitarie avanzate".
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.
See the full seriesCourse description
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.
Intended Learning Outcomes
By actively participating in this MOOC, you will achieve different intended learning outcomes (ILOs).
Week 1
- 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
ESCO: algorithms - Perform model evaluation and selection in supervised learning
Week 2
- Classify machine learning problems in unsupervised learning
- Describe the utility of dimensionality reduction techniques
- Describe the main techniques for identifying clusters of data
Week 3
- 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
Prerequisites
No prerequisites are required: however, having basic statistical notions may help you better understand some considerations.
Activities
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.
Topic outline
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Week 1 introduces the main techniques for dealing with supervised learning problems, that are classification and regression.
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Week 2 explores unsupervised learning techniques for clustering, dimensionality reduction and association rules mining.
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Week 3 introduces reinforcement learning for solving sequential decision-making problems.
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Assessment
You will have successfully completed the course if you achieve 60% (or more) of the total score in each assessed quiz. The maximum score possible for each quiz is given at the beginning of the quiz. You can view your score in the quiz on your last attempt or on the 'Grades' page. Your final grade for the course will be based on the results of your answers to the assessed quizzes you will find at the end of each week (Weekly Quizzes). You have an unlimited number of attempts at each quiz, but you must wait 15 minutes before you can try again.
Certificate
You can achieve a certificate in the form of an Open Badge for this course, if you reach at least 60% of the total score in each one of the assessed quizzes and fill in the final survey.
Once you have completed the required tasks, you will be able to access ‘Get the Open Badge’ and start issuing the badge. Instructions on how to access the badge will be sent to your e-mail address.
The Badge does not confer any academic credit, grade or degree.
Information about fees and access to materials
You can access the course absolutely free of charge and completely online.
Course faculty
Marcello Restelli
Teacher
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.
Contact details
If you have any enquiries about the course or if you need technical assistance please contact pok@polimi.it. For further information, see FAQ page.