This module considers a wide range of topics within Machine Learning, with a strong focus on providing you with an in-depth understanding of the foundations of different machine learning technologies and providing you with the skills to implement and apply these technologies to solve a wide range of problem. In the context of machine learning, the problem domains include mostly the construction of classifier models, prediction models, and then some data mining applications.
The module starts with in introduction to machine learning, and then focuses on one of the most important aspects of problem solving using machine learning algorithms, i.e. that of data engineering. This is then followed by a study of the following machine learning paradigms:
Similarity-based learning
Information-based learning
Error-based learning
Kernel-based learning
Reinforcement learning
Unsupervised learning
Ensemble learning
Online learning
Probabilistic learning
Machine learning for data mining
The module starts with in introduction to machine learning, and then focuses on one of the most important aspects of problem solving using machine learning algorithms, i.e. that of data engineering. This is then followed by a study of the following machine learning paradigms:
Similarity-based learning
Information-based learning
Error-based learning
Kernel-based learning
Reinforcement learning
Unsupervised learning
Ensemble learning
Online learning
Probabilistic learning
Machine learning for data mining
- Facilitator: ANDRIES PETRUS Engelbrecht
- Facilitator: Jean-Pierre Van Zyl