Teaching

University of Illinois Chicago (by Prof. Min Yang)

• A Friendly Introduction to Optimal Design [link]

National Taiwan University

• 資料科學方法論 Computational Methods for Data Science (Data5010) -- Spring 2023, 2024

  • • Preliminaries (Slides; Supplement) (Video, Video)

    • Data collection

      • Linear regression & Survey sampling (Slides; Supplement) (Video, Video)

      • Factorial plans (Slides) (Video, Video)

      • Space-filling and Response surface designs (Slides) (Video)

    • Data splitting techniques: SPlit, Twinning, and Optimal ratio (Slides) (Video)

    • Supervised learning: Nonparametric regression (Slides, Slides, Slides; Supplement) (Video, Video, Video)

    • Supervised learning: Classification (Slides; Supplement)(Video, Video)

    • Unsupervised learning: Clustering & PCA (Slides; Supplement)(Video, Video)

    • An Introduction to Deep Learning (Slides)

    • Statistical techniques for Big/High-dimensional data: Subsampling and Feature screening (Slides)

I spent much time teaching during my career at the Graduate Institute of Statistics, National Central University. I enjoyed it very much, especially in the discussion with passionate students. The following lists the courses and slides I have taught so far. I am grateful to the students who provided valuable feedback and suggestions. 

National Central University

• Deep Learning in Statistical Analysis (ST7066) -- Fall 2020

  • • Fundamentals of Machine Learning: Unsupervised and Supervised Techniques (Slides, Slides, Slides)

    • Deep Neural Networks: FNN, BP, SGD, Dropout (Slides, Slides)

    • Deep Learning for Computer Vision: CNN, Heat Map (Slides)

    • Deep Learning for Text and Sequences: RNN, LSTM (Slides)

    • Generative Deep Learning: Variational Auto Encoder, GAN (Slides, Slides)

• Design of Experiments I (ST6023) -- Spring 2019, 2020, 2021

  • • CRD, RBD, One/Two/Multi-Way Layout, Latin Square Design, BIBD (Slides, Slides, Slides)

    • Multi-Stratum Full/Regular/Nonregular Factorial Design, Definitive Screening Design (Slides, Slides, Slides, Slides)

    • Response Surface Methodology (Slides)

    • Robust Parameter Design (Slides)

    • Order-of-Addition Experiment, Theory of Optimal Design

• Multivariate Analysis I (ST6045) -- Spring 2018, 2019, 2020, 2021

  • • Preliminaries: Matrix Algebra, Random Sampling, and Multivariate Normal Distribution (Slides, Slides, Slides, Slides, Slides)

    • Analysis of the Mean Structure: Inference about a Mean Vector, Multivariate ANOVA, Support Vector Regression, Regression Trees (Slides, Slides, Slides, Slides)

    • Analysis of the Covariance Structure: Kriging, Treed Kriging, Principal Components, Multilinear Principal Components, Factor Analysis, Canonical Correlation Analysis (Slides, Slides, Slides, Slides, Slides, Slides)

    • Classification: K-Nearest Neighbor, Discriminant Analysis, Classification Trees, Support Vector Machines (Slides)

    • Cluster Analysis: Hierarchical Clustering, K-Means Clustering, Spectral Clustering (Slides)

    • Feature Screening and Subsampling (Slides)

    • Deep Neural Networks (Slides)

    • Resampling Methods: Cross-Validation, Bootstrap (Slides)

    • Ensemble Learning: Bagging, Random Forests, Boosting 

• Quality Engineering (ST6078) -- Fall 2019 

  • • Introduction (Slides)

    • Multi-Stratum Experiment (Slides, Slides, Slides, Slides, Slides)

    • Computer Experiment (Slides, Slides, Slides, Slides, Slides)

    • Subsampling Technique for Big Data (Slides)

    • Mixture and Order-of-addition Experiment (Slides)

    • Summary (Slides)

• Regression Analysis (ST6021) -- Fall 2018 

  • • Linear Model Estimation/Testing/Prediction/Diagnostics (Slides, Slides, Slides, Slides)

    • Generalized Least Squares, Lack-of-fit Test, Dummy variables  (Slides, Slides, Slides, Slides)

    • Variable Selection (Slides)

    • Lasso, Ridge Regression, Missing data (Slides)

    • Mixed-Effect Model, Bayesian Regression (Slides)

    • Neural Networks (Slides)