Machine Learning and Pattern Recognition II

-1. Symbolic Learning
-2. Statistical Learning
-3. Artificial Neural Networks
-4. Support Vector Machines5. Cluster Analysis
-6. Fuzzy Logic
-7. Evolutionary Computation
-8. Hybrid Intelligent Methods

Knowledge
-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.

Skills
-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.

General competence
-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.

-Lectures
-Group work
-Lab work
-Assignments
-Homework

Additional information:
-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 NTNUs learning management system (Blackboard).

Compulsory requirements: None.