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Explore comprehensive machine learning fundamentals through this university-level course covering essential algorithms, statistical methods, and modern deep learning techniques. Master foundational concepts including k-nearest neighbors, linear regression, and statistical learning theory before progressing to advanced topics such as neural networks, support vector machines, and Gaussian processes. Delve into cutting-edge deep learning architectures including convolutional neural networks, recurrent networks, attention mechanisms, and transformer models. Discover generative modeling approaches through autoencoders, variational autoencoders, and generative adversarial networks, while also examining ensemble methods like bagging and boosting. Learn practical applications through specialized guest lectures covering normalizing flows, unsupervised word translation, fact checking with reinforcement learning, sum-product networks, model compression for natural language processing, and real-world dataset analysis using Kaggle competitions. Gain hands-on experience with mixture models, expectation-maximization algorithms, hidden Markov models, kernel methods, and gradient boosting techniques essential for modern machine learning practitioners and researchers.
