Applied Signal Recognition and Classification

Syllabus

Homayoon S.M. Beigi

• Bulletin Description
• Rationale
• Pre-Requisites
• Books and Publications
• Lectures
  • 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