Graduate @ Temple: ECE9833 Explainable AI
Covering: Effective Learning Principles, Neural Complexity, Large Language Model, Saliency Map, Neural Activation Map Analysis, Gradient-weighted Class Activation, Attention, Transformer, Transparency, Machine Learning Explainability, Deep Learning Explainability, Interpretability, Decision-making Risk, Real-world Practices, etc.
Graduate @ Temple: ECE8833 Efficient AI
Covering: Efficient Learning Theories, Patterns in the Data, Critical Pattern Learning, Efficient Brain 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.
Graduate @ Temple: ECE6833 Neural Network Foundations
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.
Graduate: 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.
Undergraduate: 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.
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