Machine Learning: an overview

Politecnico di Milano

The course "Machine Learning: an overview" offered by Politecnico di Milano, delivers a comprehensive introduction to the core methods in the field of machine learning. Students will gain a broad understanding of the various problems that can be addressed through machine learning techniques and the algorithms that underpin them.

Throughout the course, participants will delve into the taxonomy of machine learning problems, distinguishing between supervised and unsupervised learning, and exploring the limitations of these techniques. Real-world examples and case studies will be used to illustrate the practical application of these concepts, providing valuable insights into when and how to employ different approaches.

Key topics covered include:

  • Classification of machine learning problems
  • Utility of dimensionality reduction techniques
  • Formulation of sequential decision-making problems
  • Optimization of policies in reinforcement learning

By the end of the course, participants will be equipped with the knowledge and skills to classify machine learning problems, understand the limitations of supervised learning, and utilize techniques such as dimensionality reduction and reinforcement learning in practical scenarios.

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Machine Learning: an overview
Course Modules

Machine Learning: an overview offers a comprehensive exploration of the main methods in the field. The modules cover supervised learning, unsupervised learning, and reinforcement learning, providing insights into problem classification and limitations, illustrated through real-world examples and case studies.

Week 1 - Supervised Learning

Week 1 - Supervised Learning

The first module introduces participants to the fundamentals of supervised learning, including the concepts of regression and classification problems. It provides insights into model selection and offers a quiz to reinforce understanding.

Week 2 - Unsupervised Learning

Week 2 - Unsupervised Learning

This module delves into unsupervised learning, covering topics such as clustering, dimensionality reduction, and association rules. Participants will gain a deep understanding of these techniques and their practical applications through a comprehensive quiz.

Week 3 - Reinforcement Learning

Week 3 - Reinforcement Learning

Participants will explore sequential decision-making problems, Markov decision processes, and reinforcement learning algorithms in this module. The comprehensive quiz will test understanding and facilitate practical application of the concepts.

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