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Multi-objective Optimization
A brief introduction to the multi-objective optimization problem, Pareto fronts, scalarization methods, and hypernetworks for Pareto set learning.
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Bayesian Optimization
An introduction to Bayesian optimization, Gaussian process surrogate models, and acquisition functions for optimizing expensive black-box functions.
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Bayesian Network
A gentle introduction to Bayesian networks and probabilistic graphical models.
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Introduction to Probability - Part 2
Mathematical concepts for probabilistic machine learning, covering maximum likelihood estimation, naive Bayes, and information theory.
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Introduction to Probability - Part 1
Introduction to statistics, probability, and distributions.
