An overview of the techniques that are transforming many industries and will change our lives. 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.
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
