Course - Machine Learning and Case-Based Reasoning - TDT4173
Machine Learning and Case-Based Reasoning
About
About the course
Course content
The course gives an introduction to the principles and methods for automatic learning in computer systems. Classical syntax-based learning methods as well as more knowledge-intensive methods are described. Main empahsis is on symbolic methods, where explicit concepts and relationships are learned. Statistical methods and reinforcement learning is also included. The strengths and weaknesses of various methods are compared.
Learning methods in case-based reasoning and the integration of learning and problem solving is given particular treatment. Numerical and cognitive models for similarity asessment will be discussed, together with different learning system architectures. Methods that combine case-based and generalisation-based inferences will be discussed as well.
Learning outcome
The aim of the course is to introduce principles of machine learning methods in general and case-based methods in particular, to students with a basic knowledge of AI methods.
Learning methods and activities
Lectures, colloquia, self study, exercises. Portfolio assessment is the basis for the grade in the course. The portfolio includes a final written exam (80%) and exercises (20%). The results for the parts are given in %-scores, while the entire portfolio is assigned a letter grade. If there is a re-sit examination, the examination form may be changed from written to oral.
Recommended previous knowledge
TDT4136 Introduction to Artificial Intelligence, TDT4171 Artificial Intelligence Methods or similar.
Course materials
Text book: Tom Mitchell: Machine learning, McGraw Hill, 1997. Scientific papers: To be determined at course start.
Credit reductions
| Course code | Reduction | From |
|---|---|---|
| IT3704 | 7.5 sp | |
| MNFIT374 | 7.5 sp | |
| MNFIT374 | 7.5 sp |
Subject areas
- Industrial Economics
- Information Security
- Informatics
- Psychology
- Statistics
- Technological subjects