Course - Artificial Intelligence Methods - TDT4171
TDT4171 - Artificial Intelligence Methods
About
Examination arrangement
Examination arrangement: School exam
Grade: Letter grades
| Evaluation | Weighting | Duration | Grade deviation | Examination aids |
|---|---|---|---|---|
| School exam | 100/100 | 4 hours | D |
Course content
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.
Learning outcome
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.
Learning methods and activities
Lectures, self study and exercises.
Compulsory assignments
- Mandatory assignments
Further on evaluation
A number of assignments are given out during the semester. A number of these must be passed to be eligible for exam. Details will be given at the start of the course.
The written exam will be given in English only.
If there is a re-sit examination, the examination form may change from written to oral.
Recommended previous knowledge
- TDT4136 Introduction to Artificial Intelligence, or equivalent.
- TMA4240 Statistics or similar
- TDT4172 Introduction to Machine Learning or similar
- Programming experience, e.g., TDT4109 - Information Technology, Introduction
Course materials
- Stuart Russel, Peter Norvig: Artificial Intelligence. A Modern Approach, Fourth Edition, Pearson, 2020.
- Ian Goodfellow and Yoshua Bengio and Aaron Courville: Deep Learning, MIT Press, 2016.
Any additional material will be distributed through the course's webpage.
Credit reductions
| Course code | Reduction | From | To |
|---|---|---|---|
| IT2702 | 3.7 | AUTUMN 2007 | |
| IT272 | 3.7 | AUTUMN 2007 | |
| MNFIT272 | 3.7 | AUTUMN 2007 | |
| TDT4170 | 3.7 | AUTUMN 2007 | |
| SIF8031 | 3.7 | AUTUMN 2007 | |
| IT3704 | 3.7 | AUTUMN 2008 | |
| MNFIT374 | 3.7 | AUTUMN 2008 | |
| MNFIT374 | 3.7 | AUTUMN 2008 |
No
Version: 1
Credits:
7.5 SP
Study level: Third-year courses, level III
Term no.: 1
Teaching semester: SPRING 2025
Language of instruction: English
Location: Ålesund , Trondheim
- Computer Systems
- Informatics
Department with academic responsibility
Department of Computer Science
Examination
Examination arrangement: School exam
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
- Spring ORD School exam 100/100 D INSPERA
-
Room Building Number of candidates - Summer UTS School exam 100/100 D INSPERA
-
Room Building Number of candidates
- * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"