Machine Learning
Core machine learning: linear models, neural networks, decision trees, ensemble methods, gradient descent, backpropagation, and deep learning.
Learning objectives
Upon completion of this course, students will: be acquainted with the dominant concepts of machine learning methods, including some theoretical background. (Knowledge and understanding) acquire knowledge of established machine learning techniques such as linear models, neural networks, decision trees and ensemble methods (Knowledge and understanding) learn some statistical techniques to assess and validate machine learning results. (Apply knowledge and understanding, make judgments)
Machine learning is the discipline that studies how to build computer programs that learn how to behave from examples, rather than following explicit instructions. It is a subfield of artificial intelligence that intersects with statistics, cognitive science, information theory, and probability theory. Recently, machine learning has become increasingly important for the design of search engines, robots, and sensor systems, and for the processing of large scientific data sets. Other applications include handwriting or speech recognition, image classification, medical diagnosis, stock market analysis and bioinformatics. Machine Learning is also the core technology behind chatbots like ChatGPT, Claude and Gemini. The course covers a wide variety of machine learning techniques, but puts particular emphasis on gradient descent optimization, backpropagation, neural networks and deep learning. Some discussion on the broader social impact of machine learning technology is included.
Assessment
The course assessment consists of two parts: an examination and a practical assignment. The examination consists of a standard exam and four online quizzes. The examination and practical assignment each comprise 50% of the final grade. To pass the course, the examination grade should be at least 5.5, the practical assignment grade should be at least 4.5 and the average should be at least 5.5. The examination is made individually, and the practical assignment is made in groups. There is a resit for the exam, no resit is possible for the practical assignment.
Teaching methods
The course consists of pre-recorded videos, interactive lecture/QA sessions (two per week) and optional homework assignments discussed in working groups (one per week). The practical assignment is supported by small exercises to help with the relevant technologies, and informal presentations at project groups (one per week). There is no mandatory attendance for any lectures or workgroups, except that one member of each group must be present at the weekly project group. Most of the material is freely available at https://mlvu.github.io The course is taught in English.
Literature
There is no textbook. Some reading material will be provided digitally.
Prerequisites
We require that students have some prior experience with linear algebra, calculus (limited to differentiation), probability theory and statistics. An overview and explanation of the required preliminaries can be found at https://mlvu.github.io/preliminaries/