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Explore fundamental machine learning concepts and algorithms through this comprehensive university-level course from the University of Utah's Data Science program. Master the theoretical foundations of machine learning starting with supervised learning setups and decision trees, then progress through linear models, perceptrons, and support vector machines. Delve into computational learning theory including mistake bound models, VC dimensions, and learnability results while understanding concepts like Occam's Razor and agnostic learning. Study advanced optimization techniques such as stochastic gradient descent and least mean squares regression, followed by ensemble methods including boosting and practical implementation strategies. Gain expertise in Bayesian learning approaches, logistic regression, and neural networks while learning to quantify learning algorithms and avoid overfitting. Conclude with hands-on practical advice for applying machine learning techniques in real-world scenarios, covering both theoretical underpinnings and implementation considerations essential for data science practitioners.
