Machine Learning School
Course details
A course in modern machine learning methods that covers theoretical and practical aspects.
NOTE REGARDING THE WORKLOAD:
The requirement is for each participant to complete 12 modules, a team project and a final exam. This works out to a workload of 180h.
Since the number of modules available in the course is greater than 12, the total number of hours is greater than 180 – but these modules are not all to be taken by the same participants.
Language
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Keywords
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Target group
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Course type
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Educational Level
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ECTS credits
6
Number of learners
30
Mode of delivery
Blended
Level of planning
Simple
Status
In planning
Course public access
Private course. Private course can be accesed by anonymous users (preview), only by share link.
Learning outcomes
The participant is able to explain basic concepts from the field of machine learning such as: machine learning, implicit and explicit knowledge representation, local and global generalization, underfitting, overfitting, bias, variance, regularization and more.
Understanding
1
The participant understands and is able to explain the principle of fundamental machine learning methods.
Understanding
1
The participant is able to assess where and how machine learning methods can be applied.
Evaluating
1
The participant is able to apply machine learning methods and approaches.
Applying
1
The participant is able to identify machine learning problems and search for corresponding state of the art methods.
Evaluating
1
Total Weight: 5
Course access
Contributors
- Michal Gregor (michal.gregor@uniza.sk)
