Teaching at CUHK-SZ
Teaching at CUHK-SZ
Lecture notes all [.pdf]
Probability theory review [.pdf], more comprehensive notes [.pdf]
Introduction: notes [.pdf], numerical examples [.pdf]
Point estimation: notes [.pdf], numerical examples [.pdf]
Confidence intervals: notes [.pdf], numerical examples [.pdf]
Hypothesis testing: notes [.pdf], numerical examples [.pdf]
Linear regression: notes [.pdf], numerical examples [.pdf]
2024 Fall: syllabus [.pdf] (generated by bb), syllabus [.pdf]
Lecture slides: overleaf folder
Lecture notes:
Introduction [.pdf]: why nonparametrics?
Classical nonparametric tests [.pdf]: Wilcoxon tests, KS test, rank correlations, KM estimator, log-rank test
Nonparametric estimation and smoothing [.pdf]: EDFs, bootstrap, KDE, local regression, splines, RKHS methods
Bayesian nonparametrics [.pdf]: Dirichlet processes, Gaussian processes
Advanced models and applications [.pdf]: hierarchical DPM, spatial models, GP learning on manifolds, BO
2025 Spring: syllabus [.pdf]
2026 Spring: syllabus [.pdf]
2026 Fall: syllabus [.pdf]
2025 Spring: 4 students (1 team),
Best Poster Award (1st place out of 31 teams)
2026 Spring: 15 students (6 of which co-advised with Tianyang Hu, 4 teams),
Outstanding Presentation Award (top 5 out of 45 teams) x2
Meritorious Presentation Award (top 15 out of 45 teams)
Data Detective 101: Spotting Statistical Traps [.pdf]
Lecture notes [.pdf],
R code:
the birthday problem [.r],
stock selection by mean and variance [.pdf] [.r] [.png]
2022 Fall: syllabus [.pdf], practice final [.pdf], final exam [.pdf]
Results of the experiment guessing 2/3 of the average [wiki]: [.png]
2024 Winter: syllabus [.pdf],
Results of the Blotto game [wiki]: round 1 [.csv], round 2 [.csv]
Results of the experiment guessing 2/3 of the average [wiki]: [.png]
Lecture notes[.pdf],
math quiz[.pdf],
selected problems[.pdf]
notability notes (~21MB): 2024 Spring [.pdf], 2024 Summer [.pdf]
2022 Winter: syllabus[.pdf], practice final[.pdf], final exam[.pdf]
2022 Spring: syllabus[.pdf], practice midterm[.pdf], midterm exam[.pdf], final exam[.pdf]
2023 Summer: syllabus[.pdf], midterm exam[.pdf], final exam[.pdf]
2023 Fall: syllabus [.pdf], midterm exam 1[.pdf], practice midterm exam 2 [.pdf], midterm exam 2 [.pdf], final exam [.pdf]
2024 Spring: syllabus [.pdf], midterm exam 1 [.pdf], midterm exam 2 [.pdf], final exam [.pdf]
2024 Summer: syllabus [.pdf], midterm exam 1 [.pdf], midterm exam 2 [.pdf], final exam [.pdf]
Lecture notes [.pdf],
math quiz [.pdf]
R code:
method of moments [.r],
MLE [.r], MLE Asymptotic Normality & CI [.r],
bootstrap [.r],
p value [.r],
Bayesian methods [.r], Gibbs sampler of the Gaussian Mixture Model [.r]
2023 Winter: syllabus [.pdf], practice final [.pdf], final exam [.pdf]
Data project: questions.pdf, (synthetic) data[.csv]
2023 Spring: syllabus [.pdf], final exam [.pdf], IC final exam [.pdf]
Data project: questions.pdf, Santa Cruz rainfall days data [.pdf] [website]
First year exam review, 2020 Summer, syllabus and handout