The course 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 course 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.
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About this Course
No prerequisites are required: however, having basic statistical notions may help you better understand some considerations.
What you will learn
Classify machine learning problems, supervised learning problems and describe the limitations of machine learning techniques in supervised learning
Classify machine learning problems in unsupervised learning, describe the utility of dimensionality reduction techniques
Formulate a sequential decision-making problem, explain what a value function is and 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.
Offered by

Politecnico di Milano
Politecnico di Milano is a scientific-technological University, which trains engineers, architects and industrial designers.
Syllabus - What you will learn from this course
Week 1 - Supervised Learning
Week 2 - Unsupervised Learning
Week 3 - Reinforcement Learning
About the Artificial Intelligence: an Overview Specialization
This Specialization is intended for beginners seeking to enter the artificial intelligence world. Through five courses, you will cover artificial intelligence technical groundings (including machine learning and technologies), ethical and legal issues, which will give you a clear picture of what artificial intelligence is and what opportunities artificial intelligence will provide in the next future.

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