Course - Computational Intelligence - IMT6101
IMT6101 - Computational Intelligence
About
This course is no longer taught and is only available for examination.
Examination arrangement
Examination arrangement: Portfolio assessment and Written exam
Grade: Passed / Not Passed
Evaluation | Weighting | Duration | Grade deviation | Examination aids |
---|---|---|---|---|
Portfolio assessment | 50/100 | |||
School exam | 50/100 | 3 hours |
Course content
-Symbolic Learning -Statistical Learning -Artificial Neural Networks -Support Vector Machines -Cluster Analysis -Fuzzy Logic -Evolutionary Computation -Hybrid Intelligent Methods
Learning outcome
Knowledge: On concluding the course, candidates -will have an in-depth understanding of theories, methods, and algorithms in machine learning. -will be able to apply the most appropriate machine learning algorithms in various applications.
Skills: On concluding the course, candidates -will be able to evaluate and contrast basic techniques and algorithms used in machine learning. -will be able to formulate specific algorithmic requirements for a given problem and propose an appropriate solution. -will be able to predict and judge the performance of a machine learning or a data mining method.
General Competence: On concluding the course, candidates -will be able to assess the nature of a problem at hand and determine whether a machine learning technique/algorithm can solve it efficiently enough. -will strengthen their ability to work with the original scientific literature.
Learning methods and activities
Lectures Homework
Further on evaluation
Re-sit examination: For the written exam: Ordinary re-sit examination.
Specific conditions
Admission to a programme of study is required:
Computer Science (PHD-CS)
Information Security (PHD-IS)
Information Security and Communication Technology (PHISCT)
Recommended previous knowledge
IMT4133 Data Science for Security and Forensics or at least similar master level course.
Course materials
Basic Textbook: Pattern Recognition and Machine Learning (Information Science and Statistics) by Christopher M. Bishop Pattern Classification (2nd Edition) by Richard O. Duda, Peter E. Hart, and David G. Stork Selected research papers Additional Literature for interested readers: Machine Learning and Data Mining: Introduction to Principles and Algorithms by Igor Kononenko, Matjaz Kukar Machine Learning by Tom M. Mitchell
No
Version: 1
Credits:
5.0 SP
Study level: Doctoral degree level
Language of instruction: English
Location: Gjøvik
- Informatics
Department with academic responsibility
Department of Information Security and Communication Technology
Examination
Examination arrangement: Portfolio assessment and Written exam
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
-
Autumn
ORD
Portfolio assessment
50/100
Submission
2023-12-20
14:00 -
Room Building Number of candidates - Autumn ORD School exam 50/100 2023-12-21 09:00 PAPIR
-
Room Building Number of candidates B211- 2.etg. Beryll 2
- * 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"