Machine Learning A
Course description
Content
The course introduces basic theory and algorithms of machine learning. The course covers the following tentative list of topics:
- Supervised learning setting
- Unsupervised learning setting
- Concentration of measure inequalities
- Analysis of generalization in classification
- Algorithms
Prerequisites
- Knowledge of Linear Algebra corresponding to Lineær algebra i datalogi course (LinAlgDat)
- Knowledge of Calculus corresponding to Introduktion til matematik i naturvidenskab (MatintroNat) or Matematisk analyse og sandsynlighedsteori i datalogi (MASD).
- Knowledge of Probability Theory corresponding to Sandsynligheds-regning og statistik (SS), Grundlæggende statistik og sandsynlighedsregning (GSS) or Matematisk analyse og sandsynlighedsteori i datalogi (MASD) and Modelling analysis of data (MAD).
- Knowledge of Discrete Mathematics corresponding to Diskret matematik og formelle sprog (DMFS) or Diskret Matematik og algoritmer (DMA).
- Knowledge of programming corresponding to Programmering og problemløsning (PoP) and experience with programming in Python.
You can test your skills by solving the self-assessment assignment at https://sites.google.com/diku.edu/machine-learning-courses/ml-a.
Practical information
The course is offered as a single-subject course at the Faculty of Science. Single-subject courses typically run for seven to nine weeks with scheduled activities approx. one day per week.
You will be attending the course together with full-time students from the Faculty of Science, and you must meet various admission requirements. The course ends with an exam.
The number of places is limited, and they are allocated on a first-come, first-served basis, so do not delay your application!
Location
University of Copenhagen
- Department of Computer Science
* in some semesters, the course may be run from another location. You will be informed about this upon registration.
Contact
SCIENCE Student Services
Tel: +45 35 33 35 33 - kl. 9.12:30 (closed Wednesdays)
E-mail: studenterservice@science.ku.dk
