Machine Learning (POK)

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.

By actively participating in this MOOC, you will achieve different intended learning outcomes (ILOs).

Week 1

Classify machine learning problems

Classify supervised learning problems

Describe the limitations of machine learning techniques in supervised learning

Identify the key elements of supervised learning 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