This is an advanced-level self-standing learning module on Machine Learning.
The module (10-20 hours) builds on fundamental knowledge and focuses on the design, training, and evaluation of machine learning models. It explores major algorithms for classification, regression, clustering, and dimensionality reduction, as well as more specialized topics such as ensemble methods, neural networks, and deep learning basics. Learners will also be introduced to advanced concepts such as model evaluation metrics, hyperparameter tuning, and cross-validation.
By the end of the course, participants will:
- Gain knowledge of the mathematical foundations and algorithmic principles behind common ML methods.
- Apply advanced techniques to real-world datasets and critically interpret results.
- Understand model selection, validation, and optimization strategies.
Reflect on the challenges of fairness, transparency, and ethical deployment of ML systems.
Intended Learning Outcomes
| ILO2 | Machine Learning | To describe and explain fundamentals of machine learning and the classical concepts for algorithm training. Also be able to apply machine learning approaches to solve simple problems on small datasets. |
| ILO4 | Software Tools | To describe and explain AI software languages and formulation of the algorithms. Be able to use state-of-the-art/most recent deep learning framework and an open-source neural network software to solve demonstrative problems on small datasets. |
| ILO10 | Natural Language Processing | To describe and explain fundamentals of specific tools and methods of Natural Language Processing and Language-based Foundation Models. |
Course Content
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Course Includes
- 10 Lessons
- 20 Topics
- 10 Quizzes
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