Machine Learning
Master's level course, University of Copenhagen, Department of Computer Science, 2020
Description of the course
The amount and complexity of data is rapidly increasing, and computing systems are needed to convert this data into knowledge. Machine learning develops algorithms for analyzing data to make predictions, classifications, and recommendations. These algorithms are already embedded in many systems, such as search engines, recommender systems, and biometric applications. Machine learning is widely applicable across fields like data mining, digital image analysis, natural language processing, bioinformatics, and more.
This course introduces students to the fundamental theory and techniques of statistical machine learning, providing a working knowledge of the field.
The course is designed for computer science students and others with a strong mathematical background and programming skills (e.g., Statistics, Economics, Physics, Bioinformatics).
Key topics include:
- Foundations of statistical learning
- Generalization performance (Occam’s razor, VC analysis)
- Classification methods (linear models, KNN, kernel methods, neural networks)
- Regression techniques (linear and non-linear)
- Clustering, dimensionality reduction, and visualization (e.g., PCA)
Capacity
Contributed as a Teaching Assistant. The course was taught by Yevgeny Seldin and Christian Igel