The goal of this course is to provide theoretical and methodological knowledge in machine learning. The course will explain the basic grounding in concepts such as training and tests sets, over-fitting, regularization, kernels, and loss function etc. The focus of this course will be introducing a range of model based and algorithmic machine learning methods including regression, decision trees, naive Bayes, neural network, clustering, and reinforcement learning. Some other topics will also be covered including deep learning, topic modelling (latent dirichlet allocation), and optimization (gradient descending). To understand how machine learning algorithm is designed and evaluated, the course will cover the complete process of data collection, feature creation, algorithms, and evaluation in real applications (e.g., text classification, search engine, and recommendation system). Hands-on assignments are mandatory in this course, where some machine learning tools will be roughly introduced. The expected learning outcomes include gaining theoretical knowledge about machine learning and the practical experience designing/implementing machine learning algorithms.
Nowadays, you may find a significant amount of machine learning contents especially online (e.g., toolkit, online courses, books, papers etc.), this course will mainly give an overview of machine learning on fundamental knowledge (i.e., concepts, techniques, and algorithmic models) and how some of these algorithms have been applied in the practical applications (e.g., text mining, information retrieval, semantic Web)
Included in the requreiments to the course it is asssumed that you as a student have basic knowledge of Linear Algebra, Calculus, Probability, and that you are proficient in at least one programming language, Python is preferred, which will be used in the course assignments.
Univ: To be admitted you must have (or equivalent) 90 ECTS-credits including 60 ECTS-credits in Computing Science or two years of completed studies within a study programme (120 ECTS-credits). In both cases, including at least 7.5 ECTS-credits in artificial intelligence (e.g. 5DV121) and at least 7.5 ECTS-credits in mathematical statistics, (e.g. 5MS045).
Proficiency in English equivalent to Swedish upper Secondary course English A/5. Where the language of instruction is Swedish, applicants must prove proficiency in Swedish to the level required for basic eligibility for higher studies.
Applicants in some programs at Umeå University have guaranteed admission to this course. The number of places for a single course may therefore be limited.
Application deadline was
15 October 2020.
Please note: This second application round is intended only for EU/EEA/Swiss citizens.