Much of the recent successes within the field of artificial intelligence (AI) in the last 10-15 years can be attributed to a subarea of AI called machine learning. Machine learning deals with algorithms and methods that learn to perform tasks by using data. The tasks can be anything from detecting an anomalous temperature rise from a sensor, fraudulent money transfers, or email spam to predicting the presence of a disease or to predict which direction to steer a self-driving car, and the data can be anything from surveys, polls, and counts to images, text, and sound.
This course is an introduction to machine learning for professionals in industry and public organisations who have knowledge in engineering, mathematics/statistics, computer science, or related fields. This course gives an introduction to some of the most common and well-established tools and methods in machine learning, shows the utility of such methods for solving practical problems, and intends to provide the tools needed to use and develop machine learning-based solutions for your own data problems.
The course will cover many of the main machine learning methods, and the associated theory and algorithms, ranging from classical regression and classification methods to more recent developments in deep learning.
The three main learning outcomes for students attending this course are:
– Describe and explain a subset of the concepts and methods that are central in machine learning, such as classification, regression, clustering, dimensionality reduction, bias/variance, over- and underfitting, etc.
– Categorise machine learning methods based on the needs of the data problem at hand, be able to select an appropriate machine learning method for a task, and to properly evaluate a chosen machine learning method.
– Apply your knowledge using modern and state-of-the-art machine learning libraries on real data.
The course corresponds to 3 ECTS credits and consists of seven lectures and practical exercises. The course is divided over three days of lectures and exercises, and a fourth day with final lecture and presentations of home assignments. The course is developed at the Department of Computing Science, Umeå University, and is part of AI Competence for Sweden[. A certificate of attendance will be provided, but no formal credits will be possible to obtain in this edition of the course. Note that presence during the course days and submitting the home assignments is mandatory to receive the certificate.
Dates during 2021: October 22, November 5, November 19, and December 10 (presentations of homework)
Location: Seminar room at MIT-Place, in the MIT building, or through Zoom.
Register using this link before September 17.
For more information, please, contact Tommy Löfstedt: firstname.lastname@example.org
The course is part of a package of introductory courses for the industry and public organisations that is currently being developed by Umeå University for increasing knowledge in the field of artificial intelligence. For an overview, please, visit this page.