Machine Learning 1 - 2020

Machine Learning 1 - 2020

Dive into a comprehensive 21-hour lecture series on machine learning, developed by the Amsterdam Machine Learning Lab at the University of Amsterdam. Follow the Pattern Recognition and Machine Learning book by Bishop as you explore fundamental concepts and advanced techniques. Begin with an introduction to machine learning types and probability theory before progressing to topics such as linear regression, model selection, neural networks, and unsupervised learning. Master key algorithms including stochastic gradient descent, logistic regression, and support vector machines. Delve into clustering methods, principal component analysis, and Gaussian processes. Conclude with an examination of model combination methods, bootstrapping, and decision trees. Gain a solid foundation in machine learning theory and practice through this comprehensive course taught by dr.ir. Erik Bekkers.