Graduate: ECE 595 Efficient AI Theories and Designs

Covering: Efficient Learning Theories, Patterns in the Data, Critical Pattern Learning, Computation Efficiency, Efficient Pattern Abstraction, Model Complexity, Efficient Model Execution, Learning Optimization, Efficient and Effective Inference, Learning Machine Deployment, Real-world System Design, Real-time Inference, etc.

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Graduate: ECE 629 Neural Network

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

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Graduate: ECE 662 Pattern Recognition and Decision Making

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.

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Undergraduate: ECE 301 Signals and Systems

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