TDT4173 - Machine Learning and Case-Based Reasoning


Examination arrangement

Examination arrangement: Portfolio assessment
Grade: Letters

Evaluation Weighting Duration Grade deviation Examination aids
Arbeider 20/100
Skriftlig eksamen 80/100 4 timer

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.

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 To
IT3704 7.5
MNFIT374 7.5
MNFIT374 7.5
More on the course



Version: 1
Credits:  7.5 SP
Study level: Second degree level


Term no.: 1
Teaching semester:  SPRING 2016

Language of instruction: English


Subject area(s)
  • Industrial Economics
  • Information Security
  • Informatics
  • Psychology
  • Statistics
  • Technological subjects
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Computer Science


Examination arrangement: Portfolio assessment

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Spring ORD Arbeider 20/100
Room Building Number of candidates
Spring ORD Skriftlig eksamen 80/100 2016-06-08 09:00
Room Building Number of candidates
Summer KONT Arbeider 20/100
Room Building Number of candidates
Summer KONT Oral examination 80/100 2016-08-08
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"

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