IMT4612 Machine Learning and Pattern Recognition 1
Expected learning outcomes
- The candidate possesses advanced knowledge in pattern recognition systems (symbolic and statistical learning, artificial neural networks, support vector machines, clustering, fuzzy techniques in artificial intelligence, as well as evolutionary computation and hybrid intelligent methods in artificial intelligence).
- The candidate possesses thorough knowledge about theory and scientific methods relevant for machine learning and pattern recognition.
- The candidate is capable of applying his/her knowledge in new fields of machine learning and pattern recognition.
- The candidate is capable of analyzing existing theories, methods and interpretations in the field of machine learning and pattern recognition and working independently on solving theoretical and practical problems.
- The candidate can use relevant scientific methods in independent research and development in machine learning and pattern recognition.
- The candidate is capable of performing critical analysis of various literature sources and applying them in structuring and formulating scientific reasoning in the field of machine learning and pattern recognition.
- The candidate is capable of carrying out an independent limited research or development project in machine learning and pattern recognition under supervision, following the applicable ethical rules.
- The candidate is capable of analyzing relevant professional and research ethical problems in machine learning and pattern recognition.
- The candidate is capable of applying his/her machine learning and pattern recognition knowledge and skills in new fields, in order to accomplish advanced tasks and projects.
- The candidate can work independently and is familiar with terminology in the field of machine learning and pattern recognition.
- The candidate is capable of discussing professional problems, analyses and conclusions in the field of machine learning and pattern recognition, both with specialists and with general audience.
- The candidate is capable of contributing to innovation and innovation processes.
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 (additional text)
Annet - homework
The course will be made accessible for both campus and remote students. Every student is free to choose the pedagogic arrangement form that is best fitted for her/his own requirement. The lectures in the course will be given on campus and are open for both categories of students. All the lectures will also be available on Internet through GUC’s learning management system (ClassFronter).
Form(s) of Assessment
Written exam, 3 hours
Form(s) of Assessment (additional text)
- Written exam, 3 hours (60%)
- Homework evaluation (4x10%)
- All parts must be passed.
Alphabetical Scale, A(best) – F (fail)
Internal examiner. An external examiner will be used every 4th year. Next time in the school-year 2013/2014.
For the written exam: Re-sit August 2016
Code D: No printed or hand-written support material is allowed. A specific basic calculator is allowed.
Read more about permitted examination aids.
Basic Textbook: Machine Learning and Data Mining: Introduction to Principles
and Algorithms (Paperback) by Igor Kononenko (Author), Matjaz Kukar (Author)
+ selected research papers
Additional Literature for interested readers:
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
Machine Learning by Tom M. Mitchell
In case there will be less than 5 students that will apply for the course, it will be at the discretion of Course responsibility whether the course will be offered or not an if yes, in which form.