Course - Machine Learning - TDT4173
TDT4173 - Machine Learning
About
Examination arrangement
Examination arrangement: Portfolio assessment
Grade: Letter grades
| Evaluation | Weighting | Duration | Grade deviation | Examination aids |
|---|---|---|---|---|
| Assignment | 35/100 | |||
| Project | 50/100 | |||
| Individual assignment | 15/100 |
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 generalizations, time series methods, ensemble methods, and deep learning are also included. The strengths and weaknesses of various methods are discussed. Learning methods in case-based reasoning is integrated with problem solving within the CBR cycle. 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, to give an understanding of basic mechanisms underlying various specific methods. In case-based reasoning the integration of learning and problem solving is focused.
Learning methods and activities
Lectures, group work, colloquia, self study, exercises.
Further on evaluation
Portfolio assessment is the basis for the grade in the course. The portfolio includes a method paper (35%), an individual assignment (review/quiz/etc.) (15%) and a final project (50%). The results for the parts are given in %-scores, while the entire portfolio is assigned a letter grade. In the case that the student receives an F/Fail as a final grade, then the student must retake the course in its entirety.
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.
- Michael M. Richer and Rosina Weber: Case-Based Reasoning, Springer, 2013.
Selected papers.
Credit reductions
| Course code | Reduction | From | To |
|---|---|---|---|
| IT3704 | 7.5 | AUTUMN 2008 | |
| MNFIT374 | 7.5 | AUTUMN 2008 | |
| MNFIT374 | 7.5 | AUTUMN 2008 |
No
Version: 1
Credits:
7.5 SP
Study level: Second degree level
Term no.: 1
Teaching semester: AUTUMN 2021
Language of instruction: English
Location: Trondheim
- Industrial Economics
- Information Security
- Informatics
- Psychology
- Statistics
- Technological subjects
Department with academic responsibility
Department of Computer Science
Examination
Examination arrangement: Portfolio assessment
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
- Autumn ORD Assignment 35/100 INSPERA
-
Room Building Number of candidates - Autumn ORD Individual assignment 15/100 INSPERA
-
Room Building Number of candidates - Autumn ORD Project 50/100 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"