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Foundations of Data Science III
Preface
Introduction and Probability Review
Random Variables and Discrete Distributions
Continuous Distributions
Joint Distributions
Functions of Random Variables
Limit Theorems
Sampling
Frequentism
Introduction to Parameter Estimation
Model Fitting
Maximum Likelihood Estimation
Method of Moments Estimation
Consistency, Efficiency, Sufficiency
Confidence Intervals
Hypothesis Testing
Hypothesis Testing in Practice
Multiple Hypothesis Testing
Bayesian Statistics
Bayes’ Theorem For Distributions
Estimating Proportions
Estimating Counts
Poisson Processes
Bayesian Testing
Comparison
Classification
Inference
Conjugate Priors
Markov Chains
Monte Carlo Simulation
Markov Chain Monte Carlo
MCMC in Practice
Hidden Markov Models
Hidden Markov Models (part 2)
Hidden Markov Models for Parts-of-Speech Tagging
Gradients
Gradient Descent
Improving Gradient Descent
Index