Machine Learning

Utbildningsfakta

Kod: 5DV238

This course is an introduction to machine learning and provides an overview of both theoretical and practical aspects of machine learning. The course introduces the basic concepts of machine learning and presents a range of different machine learning methods and models. The course also covers statistical and practical issues related to the design and evaluation of machine learning solutions. The course consists of two parts: **Part 1: Principles (4.5 credits)** This part introduces the background and some important applications of machine learning. The following models and topics will be covered: Supervised learning (classification and regression with methods like support vector machines, naive Bayes, K-nearest neighbours, decision trees, neural networks), unsupervised learning (clustering and dimensionality reduction with methods like k-means clustering, hierarchical clustering, principal components analysis, linear discriminant analysis, density estimation), and learning theory (PAC-learning, bias/variance trade-off, regularisation). Furthermore, important concepts in machine learning, such as generative/discriminative learning, the maximum likelihood and Bayesian learning paradigms, and parametric/non-parametric learning will be discussed. **Part 2: Practice (3 credits)** This part consists of practical assignments that introduce modern machine learning libraries and development tools. The students will apply some of the machine learning methods/models covered in Part 1 to solve machine learning problems in realistic applications.

Behörighet

At least 60 ECTS computing science or 120 ECTS within a program. (Students on a master's program leading to a degree in computing science are considered to fulfill this requirement.) At least 7.5 ECTS introductory programming; 7.5 ECTS data structures and algorithms; 7.5 ECTS mathematics including limits, derivatives, and probability theory; 7.5 ECTS linear algebra; 7.5 ECTS mathematical statistics.

Utbildningstillfällen

Varje tillfälle har egna datum och villkor. Avslutade tillfällen behålls som historik och innebär inte att en ny ansökan är öppen.

• Machine Learning

  Umeå University

  Startdatum: 18 januari 2027

  Slutdatum: 24 mars 2027

  Studietakt: 50 %