Artificial Intelligence Methods

This course is a continuation of TDT4136 Introduction to Artificial Intelligence and TDT4172 Introduction to Machine Learning.

The three main ways of reasoning (rule-based, model-based, and case-based), will be discussed, with most focus given to model-based reasoning. In particular, we work with reasoning based with uncertain and/or partly missing information. The reasoning frameworks that are most prominent in this part of the course are Bayesian networks and decision graphs. Thereafter, we discuss modern techniques for machine learning.

Knowledge:

• General principles for artificial intelligence (AI)
• Efficient representation of uncertain knowledge
• Decision making principles
• Learning/adaptive systems.

Skills:

• Assess different frameworks for AI in given contexts
• Build systems that realises aspects of intelligent behaviour in computer systems.

General competence:

• Know AI's basis taken from mathematics, logic and cognitive sciences.

Lectures, self study and exercises.