Computational Intelligence
2015-2016 - IMT6101 - 5 ECTS

Prerequisite(s)

IMT4612 Machine Learning and Pattern Recognition I

IMT4632 Machine Learning and Pattern Recognition II,

or similar courses passed outside Gjøvik University College

Expected learning outcomes

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.

Topic(s)

1. Symbolic Learning
 2. Statistical Learning
 3. Artificial Neural Networks
 4. Support Vector Machines
 5. Cluster Analysis
 6. Fuzzy Logic
 7. Evolutionary Computation
 8. Hybrid Intelligent Methods

Teaching Methods

Other

Teaching Methods (additional text)

• Lectures
• Homework

Form(s) of Assessment

Other

Form(s) of Assessment (additional text)

• Written exam, 3 hours
• Homework evaluation (4)
• All parts must be passed.

Grading Scale

Pass/Failure

External/internal examiner

Internal examiner, evaluated by the lecturer(s)

The written exam will be evaluated by external examiner (or internal and external) within 5 years period, next time at latest in 2019.

Re-sit examination

For the written exam: Ordinary re-sit examination.

Examination support

None

Coursework Requirements

None

Teaching 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

Additional information

In case there will be less than 5 students that will apply for the course, it will be at the discretion of Studieprogramansvarlig whether the course will be offered or not an if yes, in which form.