Computer Science, Advanced Technologies for Intelligent Systems, Second Cycle, 15 Credits
This course consists of two modules: Machine Learning and Probabilistic Robotics. Machine Learning is about modern methods used in computer vision, image processing, speech recognition, bioinformatics, etc. This module 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 algorithms and techniques are implemented from scratch in Matlab or Octave. Probabilistic Robotics covers modern probabilistic methods for mobile robots, with a particular focus on mapping and localisation (SLAM). By explicitly considering the probability that a robot is in a certain state or that its environment has certain properties, it is possible to develop more robust methods that work well in an uncertain world. In this course you will learn a number of probabilistic methods for localisation and mapping, as well as their mathematical underpinnings.
Level of education
Second cycle, has second-cycle course/s as entry requirements (A1F)
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), 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, as well as at least 7.5 credits from second-cycle courses that include programming or mathematical statistics.
Selection: Academic points
Application code: V5163