Course AMI23G

Machine Learning

7.5 Credits
Second Cycle

Starts week 35, 2021

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:
wk35, 2021 - wk44, 2021
Study Rate:
Time of Day:
Teaching form:
Entry Qualifications:
  • 30 credits Second Cycle within the Mainfield of Microdata Analysis or equivalent knowledge
Application Code:
Main field of study:
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