Time and location: We meet at 7:00 pm - 8:20 pm in GHC 4307 on Mar 21, Mar 28, Apr 4, Apr 11, Apr 18, Apr 25 (6 lectures). Course materials: a handout and a lecture note on Canvas Homeworks:
on Canvas
submit on Gradescope
due every Friday at 1 pm with no extension
lowest homework grade is dropped
solutions outside of Weina's office GHC 9231
Office hours: 259 office hours of Weina, Tianxin, and Alec
Grading:
Weekly homework (~2 problems/homework) -- 60% (drop lowest)
Three quizzes -- 40% (drop lowest)
Lecture 1: Basics of statistics: data generating models, common problem formulations and goals – Point estimation of parameters
Likelihood function, log likelihood
Maximum likelihood estimator (MLE)
Lecture 2: Bayesian estimation
Priors
Bayes update, maximum a posteriori (MAP) estimator
Error metrics, minimum mean square error (MMSE) estimator
Lecture 3: Hypothesis testing
Hypotheses, samples
Type I and type II errors
ML decision rule, MAP decision rule ∗ Minimum cost testing
Lecture 4: Confidence interval
Point estimation vs interval estimation
Sampling theory
Connection with hypothesis testing
Lecture 5: Classification
Problem formulation, classifiers
Error rate, empirical/training error
Bayes classifier, regression functions
Plug-in classifier
Classifiers used in practice
Lecture 6: Regression
Linear regression, least squared error
Linear regression of a random variable on a random variable
Least squared error (LSE) estimator
Linear regression with Gaussian noise
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