Teaching

Graduate: ECE 629 Neural Network @ Fall 20

Covering: Artificial Neural Network, Backpropagation Learning, Optimization Problems, Recurrent Network, Convergence Analysis, Feedforward and Multistage Networks, Reinforcement Learning, Neural Activation Functions, Learning Theories, Supervised Neural Learning, Unsupervised Neural Learning, etc.


Graduate: ECE 662 Pattern Recognition @ Spring 21 + Spring 20

Covering: Machine Learning Theories, Fundamental Learning Problems and Principles, Mathematical Optimization, Data Analysis, Feature Extraction, Statistical Analysis, Supervised Learning, Unsupervised Learning, Parametric Classifiers, Non-parametric Classifiers, Bayesian Decision Theory, etc.


Undergraduate: ECE 301 Signals and Systems @ Spring 21 + Fall 20 + Fall 19 + Spring 19 + Fall 18

Covering: Fourier Theories and Methods, Convolution, Signal Manipulation, Engineering Modeling, System Modeling, Model Simulation, Time-domain Analysis, Frequency-domain Analysis, Complex System Analysis, Convolution, Filtering, Laplace Transform, Z Transform, State Space Analysis, etc.