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Lectures

The following is a list of lecture contents for 12 weeks. Each lecture is illustrated with practical applications as indicated in the bulletin description.

Subsections

Lecture 1: Introduction (Generation of Signals and Modeling)
Lecture 2: Feature Extraction Techniques
Lecture 3: Metrics and Distortion Measures for Signal Comparison
Lecture 4: Signal Classification
Lecture 5: Parameter Estimation, Supervised Clustering and Learning Techniques
Lecture 6: Probabililty Densities and Unsupervised Clustering
Lecture 7: Hierarchical Clustering Techniques
Midterm
Lecture 8: Nonlinear Optimization Algorithms and Handling Constraints
Lecture 9: Scaling and Time-Alignment Techniques
Lecture 10: Signals Viewed as Codes Emitted from Natural Sources and Metrics of Information
Lecture 11: Markov Modeling - Practical Training and Decoding Algorithms
Lecture 12: Search Techniques for Extremely Large-Scale Systems
Final Exam

Homayoon S.M. Beigi 2003-12-12