Target group for the seminar is master level students, including master programs from other departments / faculties. Recommended prerequisites: Bachelor's in computer science or any engineering field (or similar). Programming skills. MAT-0001 / MAT-1001, MAT-1005, STA-0001 / STA-1001, MAT-1004.
Associate Professor Dilip K. Prasad, Department of computer science (UiT) will be responsible for the course. The language of instruction is English and all of the syllabus material is in English.
Computational Intelligence (CI) is inspired from statistical, pattern recognition, neural network, machine learning, fuzzy logic, evolutionary computing, scientific visualization and other sources. This course covers basic CI techniques (details below), the use of a free software package WEKA, the use of a commercial software package MATLAB, and examples of practical applications of CI methods for data in technical, medical and bioinformatics domains.
The course addresses basic computational intelligence techniques for analyzing and designing approaches for different types of problems and applications of CI. It will also discuss emerging trends of computational intelligence from the international research front. Particularly, the following topics will be addressed:
- Overview of computational intelligence, types of adaptive systems, learning and applications;
- Data visualization, exploration, and analysis: Principal Component Analysis (PCA), Multidimensional Scaling (MDS), Self-Organized Mappings (SOM), parallel coordinates and other visualization algorithms;
- Theory: overview of statistical approaches to learning, bias-variance decomposition, expectation maximization algorithm, model selection, evaluation of results, ROC curves;
- Introduction to WEKA software packages, presentation of algorithms available in the package;
- Statistical algorithms for data analysis beyond classification: discriminant analysis - linear (LDA), Fisher (FDA), regularized (RDA), probabilistic data modelling, kernel methods;
- Neural Network for offline and online learning; Mamdani and Takegi-Sugeno-Kang models, Fuzzy inference systems, and convolutional neural networks;
- Evolutionary computation, Genetic Algorithm, Particle Swarm Optimization;
- Analyze and apply CI techniques for solving classification and regression tasks using Weka/Matlab
The focus is on using computational intelligence systems and techniques to analyze data and develop approaches for solution of different problems.
There will be 4 lab-sessions, one for WEKA (2.5 hours) and three for Matlab (2.5 hours each).
Course objectives and other course info:
Additional details about the seminar can be found at https://uit.no/utdanning/emner/emne/619075/inf-3910-7.
If you have professional / technical questions you can contact Associate Professor Dilip K. Prasad.
INF-3910-7 CSS: Computational Intelligence and its Applications
The allocated time table for the three seminars will not collide with lectures / study groups for the following computer science courses: NF-3200 and INF-3201.
Master level students on a 2- or 5-year master program in computer science at UiT must register for the exam through the Studentweb within September 1st 2019. Other categories of students must contact the administration at Department of Computer Science.
PhD students at UiT
If this seminar is considered relevant as part of the academic training component for a PhD student the existing course code INF-8810 Ph.d. Special curriculum in Computer science - 10 ECTS can be used. PhD students must expect an expanded scope / depth in coursework / exam counting component (s) in relation to what is the case for students taking the INF-3910-7 seminar. PhD students must contact the responsible for the seminar.
Each PhD-student must use the application form (Norwegian only):