2024 spring: UA262 ODE [syllabus][lecture note 01][lecture note 02]
2023 winter: Nonstationary Time Series Analysis with Modern Signal Processing Techniques in National Cheng-Kung University, Tainan, Taiwan. [all lectures can be found in my Youtube channel]
2023 summer: Nonstationary Time Series Analysis with Modern Signal Processing Techniques in Cagliari University, Italy
2023 spring: STA542, Introduction to time series analysis (See Sakai)
2022 fall: MAT561, Numerical Linear Algebra (See Sakai)
2022 spring: STA611, Introduction to Statistical Methods (See Sakai)
2021 summer: lecture in Second International Summer School on Technologies and Signal Processing in Perinatal Medicine (TSPPM-2021) [Lecture slides/videos available via request]
2021 summer: lecture in Mathematics for Nonstationary Signals and applications in Geophysics and other fields (NoSAG21) [Lecture slides/videos available via request]
2021 spring: on dean’s leave.
2020 fall: MAT561, Numerical Linear Algebra (See Sakai)
2020 spring: STA250, Intro to Mathematical Statistics (See Sakai)
2020 spring: STA790, Time series analysis via time-frequency and complex analysis (See Sakai)
2019 fall: MAT531, analysis (See Sakai)
2019 spring: STA230, probability (See Sakai)
2018 fall: MAT531, analysis (See Sakai)
2018 spring: STA790, Geometric Statistical Learning
Lecture note 1: Summary of spectral graph theory and spectral embedding (Download)
Lecture note 2: Various spectral embedding algorithms (Download)
Lecture note 3: Kernel methods and RKHS (Download)
Lecture note 4: Background of differential geometry and Large deviation (Download)
Lecture note 5: Large deviation and asymptotical analysis (Download)
Lecture note 6: Clustering and its statistical analysis (Download)
Lecture note 7: Sensor fusion, dynamical system analysis (Download)
Lecture note 8: Representation and diffusion with the group structure (Download)
Lecture note 9: More algorithms and analysis, like t-SNE and random matrix (Download)
2017 fall: MAT230, probability (See Sakai)
Medical Information and Signal, Theory and Application 