Courses for Exchange Students (ECTS) 2018/2019
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Computer Science, Machine Learning, Second Cycle |
| Spring week 04 (2019) - 13 (2019) | CANCELLED | Appl.code: 51078 |
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Part-time, daytime class |
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ECTS credits: 7.5 |
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Level of education: Second Cycle (master level) |
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Level: A1N |
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Prerequisites: First-cycle degree of 180 credits, with Computer Science as the main field of study, and at least 15 credits in mathematics (analysis and algebra). The applicant must also have qualifications corresponding to the course "English B" or "English 6" from the Swedish Upper Secondary School.
OR First-cycle degree of 180 credits, and at least 30 credits in mathematics (analysis and algebra), as well as at least 15 credits in Computer Science or Informatics (which includes programming). The applicant must also have qualifications corresponding to the course "English B" or "English 6" from the Swedish Upper Secondary School. |
| Content: This course introduces the basic concepts, theories, and algorithms for pattern recognition and machine learning. These can be used in computer vision, image processing, speech recognition, bioinformatics, etc. The couse gives an overview and practical recommendations for the application of the many models and algorithms used in modern machine learning for classification, prediction, and clustering. The course cover both supervised and unsupervised algorithms, dimensional reduction techniques, feature extraction and selection, recommender systems, and neural networks for deep learning. The algorithms and techniques are implemented from scratch in MATLAB or Octave. |
| Teaching Methods: Teaching is given in the form of a number of lectures and mandatory seminars. |
| Assessment: Written lab reports, project work and participation in seminars. |
| Course Coordinator: Martin Magnusson |
| Course Syllabus: Spring 2019 |
| School: School of Science and Technology |