Machine Learning: an overview
Discover the fundamentals of machine learning, from supervised and unsupervised learning to reinforcement learning, with practical examples and case studies.
Discover the fundamentals of machine learning, from supervised and unsupervised learning to reinforcement learning, with practical examples and case studies.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Artificial Intelligence: an Overview Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.6
(144 ratings)
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What you'll learn
Classify machine learning problems into supervised, unsupervised, and reinforcement learning categories
Understand regression and classification problems in supervised learning
Apply dimensionality reduction and clustering techniques in unsupervised learning
Formulate sequential decision-making problems in reinforcement learning
Evaluate the limitations of different machine learning techniques
Select appropriate algorithms for specific machine learning challenges
Skills you'll gain
This course includes:
1.2 Hours PreRecorded video
3 assignments
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FullTime access
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There are 3 modules in this course
This course provides a comprehensive introduction to the field of machine learning, covering the three main paradigms: supervised learning, unsupervised learning, and reinforcement learning. Students will learn to classify different types of machine learning problems and understand the appropriate algorithmic solutions for each. The course emphasizes practical applications through examples and case studies, highlighting both the capabilities and limitations of various machine learning techniques. By the end of the course, students will have a solid foundation in machine learning concepts and be able to identify which approaches are suitable for different real-world scenarios.
Week 1 - Supervised Learning
Module 1 · 1 Hours to complete
Week 2 - Unsupervised Learning
Module 2 · 0 Hours to complete
Week 3 - Reinforcement Learning
Module 3 · 0 Hours to complete
Fee Structure
Instructor
Expert in Machine Learning and Reinforcement Learning
Marcello Restelli is an Associate Professor of Computer Engineering at the Department of Electronics, Information, and Bioengineering (DEIB) at Politecnico di Milano, where he earned his Laurea in Computer Science Engineering in 2000 and his Ph.D. in Information Engineering in 2004. He teaches courses in "Machine Learning" and "Reinforcement Learning" and serves on the board of the national Ph.D. program in Artificial Intelligence - Industry 4.0. His research primarily focuses on machine learning algorithms, specifically the development of reinforcement learning techniques and their applications in real-world scenarios, such as robotics, finance, autonomous vehicles, and water resource management. With over 150 peer-reviewed publications in prestigious international conferences and journals in machine learning and robotics, Restelli has also reviewed for several international journals and participated in the program committees of key conferences in his field, including ICML, NIPS, AAAI, and IJCAI. He is the principal investigator for multiple research projects funded by both public institutions and leading Italian companies.
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4.6 course rating
144 ratings
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