Machine Learning Fundamentals

This lecture introduces the fundamental principles and methods of machine learning, covering supervised and unsupervised learning techniques. Topics include linear regression, classification, k-nearest neighbors, feature expansion, kernel methods, deep learning, clustering, and dimensionality reduction. The course will include live lectures and exercise sessions, with online streaming and recordings available. Evaluation will be based on graded exercise sessions and a final exam. The lecture also covers the concepts of data attributes, text, speech, images, and mixed data sets used in machine learning.

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