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
Locked
AI usage levels
-
Keywords
-
Target group
-
Course type
-
Educational Level
-
ECTS credits
6
Number of learners
30
Mode of delivery
Blended
Level of planning
Simple
Status
In planning
Course public access
-
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)
