Course AMI23G

Machine Learning

7.5 Credits
Second Cycle
To Syllabus

Starts week 36, 2024

The course focuses mainly on the applied aspects of machine learning with special emphasis on neural networks and deep learning.
Initially, the course gives an introduction to machine learning and an overview of neural networks. The perceptron as the basic element for linear seperability and its limitations in classification is discussed. Then, different activation functions and the sigmoid perceptron is studied to solve non-linear classification problems.
Different types of machine learning paradigms such as supervised, unsupervised, and reinforcement learning is covered. Feed-forward neural networks and the back-propagation algorithm will be presented. The course will also cover recurrent neural networks.
Finally, deep learning is discussed with emphasis on the basic prenciples and different types of deep learning neural networks.
Starts and ends:
week 36, 2024 - week 45, 2024
Study Rate:
50%
Location:
Borlänge
Time of Day:
Day
Teaching form:
Normal
Language:
English
Other:
Only for Exchange Students (Erasmus)
Entry Qualifications :
  • 30 credits second level within the Mainfield of Microdata Analysis
Application Code:
HDA-H3J7U
Main field of study:
Literature List

Literature lists are published at the latest one month ahead of the course start date.

To Literature List
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Course Coordinator
Arend Hintze