New Hybrid Course: Machine Learning A at the University of Copenhagen
The Department of Computer Science at the University of Copenhagen is offering a hybrid course titled "Machine Learning A". This 7.5 ECTS master-level course will run in Sep-Oct 2024 and cover basic theory and algorithms of machine learning. The course is designed for students with a solid background in linear algebra, calculus, probability theory, and Python programming. It will be delivered in a hybrid format, allowing for both physical and online participation. The lectures will be streamed via Zoom and recorded, and students will have the option to attend exercise classes either in-person or online. This course is open to students from other universities and professionals from the industry. For more information, visit https://kurser.ku.dk/course/ndak22000u/ and https://sites.google.com/diku.edu/machine-learning-courses/mla.
Registration links for Credit Students, EU Students, Non-EU Students, and Continuing Education Applicants can be found on the course website. The course assumes knowledge of linear algebra, calculus, probability theory, and basic Python programming skills. Students should also be familiar with LaTeX for typing home assignments. The course will cover topics such as K-Nearest Neighbors algorithm, Perceptron, Linear Regression, Logistic Regression, Regularization and working in the feature space, Markov’s, Chebyshev’s, and Hoeffding’s inequalities, Generalization bounds for finite sets of prediction rules, Generalization bounds based on training/validation/test sets, Lower bound for generalization, Occam’s Razor bound for countable sets of prediction rules and its application to Decision Trees, Random Forests, Neural Networks, Principal Component Analysis (PCA), Clustering: k-means & k-means++ algorithms, and Non-linear Dimensionality Reduction via Stochastic Neighbor Embedding.
Contact person: Sadegh Talebi <mstalebi.edu at gmail.com>