Computer Science, Machine Learning, Second Cycle, 7,5 Credits

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.

ECTS Credits

7,5 Credits

Level of education

Second cycle, has only first-cycle course/s as entry requirements (A1N)

School

School of Science and Technology

When is the course offered?

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 6" or "English B" 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 6" or "English B" from the Swedish Upper Secondary School.

Selection: Academic points

Course syllabus

Application code: V5060