teaching
AI-related courses taught, covering key areas Pattern Recognition, Machine learning, Circuit and Electronics, Signal and System and Introduction of AI.
Course Topics
The resource library brings together circuit and electronics notes, pattern recognition studies, data engineering materials, and student-authored contributions from the earlier website. See also the dated graduate research archive.
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Pattern Recognition : Techniques for identifying patterns in data with applications in image, speech, and biometrics. -
Machine learning : Core algorithms and models for data-driven decision-making and predictive analysis -
Circuit analysis : Principles of circuit analysis and the operation of electronic components like diodes, transistors, and op-amps -
Signal processing : Techniques for analyzing, filtering, and transforming signals in both time and frequency domains -
AI Essentials : Fundamentals of artificial intelligence, covering problem-solving, search algorithms, and basic machine learning concepts -
Image Processing : Original notes, diagrams, and code for selected topics in digital image processing. -
Data Engineering : Introduction to data engineering, data lifecycles, pipelines, storage, and practical exercises.
Additional teaching materials
- Data acquisition, processing and management
- AI for high-school students
- AI applications in organizations
The resource library also groups student contributions in circuits, electronics, pattern recognition and machine learning, alongside the research toolkit.