Goal
Upon successful completion of the course, the student will be able to
- Explain fundamental concepts and fundamentals regarding machine learning and the parameterization/implementation/evaluation of machine learning algorithms on datasets.
- Know, configure and apply the most basic machine learning algorithms per problem type category (regression, classification, clustering).
- Combine algorithms to generate solutions to real problems.
- Know the methodology of applying machine learning algorithms to data, comparing and choosing the appropriate algorithm.
- Select, analyze and compare algorithms for application to real problems
- Analyze, visualize and process datasets to find appropriate features to represent problems.
- Communicate machine learning ideas in a clear, concise and formal manner.
Aiming to apply and evaluate machine learning algorithms to problems, and explain their operation Also, the course targets to the following general competencies:
- Ability to organize and plan work and manage time effectively
- Ability to document and communicate effectively (oral and written)
- Ability to solve problems
- Ability to develop critical thinking and capacity for critical approaches
- Ability to work in a team
- Ability of interdisciplinary approaches
- Ability to apply theoretical knowledge in practice
- Ability to research
- Ability to adapt methods and techniques to new situations and conditions
- Αbility to generate new ideas – Creativity
Contents
- Introduction to machine learning
- Linear Regression
- Decision trees
- Logistic Regression
- Clustering and valuation metrics
- Naive Bayes, Support Vector Machines
- Methodology for applying machine learning algorithms
- Engineering/Feature selection, dimensionality reduction, ensembles
- Introduction to reinforcement learning and basic policy learning algorithms in discrete state-action space
- Reinforcement learning in partial perception environments and multi-agent reinforcement learning
Bibliography
- Christopher Bishop, Pattern Recognition and Machine Learning. Springer 2007.
- “Reinforcement Learning: An Introduction”, Richard S. Sutton and Andrew G. Barto Second Edition (see here for the first edition) MIT Press, Cambridge, MA, 2018
- Additional research articles on the topics of the lectures
