DATA 102
Updated 528 days ago
Lab 2: M 3-4, Etcheverry Hall 3105 OH: Tu 3-5, Warren Hall 101B
This course develops the probabilistic foundations of inference in data science. It builds a comprehensive view of the decision-making and modeling life cycle in data science, including its human, social, and ethical implications. Topics include: frequentist and Bayesian decision-making, permutation testing, false discovery rate, probabilistic interpretations of models, Bayesian hierarchical models, basics of experimental design, confidence intervals, causal inference, robustness, Thompson sampling, optimal control, Q-learning, differential privacy, fairness in classification, recommendation systems and an introduction to machine learning tools including decision trees, neural networks and ensemble methods.