Teaching & Education
Transforming AI education through experiential learning, industry relevance, and research-led instruction.
Teaching Philosophy
My approach to education blends rigorous theoretical foundations with practical, hands-on implementation. I aim to cultivate critical thinking and problem-solving skills, preparing students not just to use AI tools, but to understand their underlying mechanics and innovate upon them.
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Experiential Learning
Bridging theory and practice through real-world projects and coding assignments.
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Industry Relevance
Updating curriculum with the latest advancements in Deep Learning and computer vision frameworks.
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Research-Led Teaching
Integrating ongoing research challenges into classroom discussions to spark innovation.
Courses
Introduction to Programming (CS101)
Foundations of programming, algorithmic thinking, and problem-solving using Python/C++.
Data Structures & Algorithms (CS201)
Core data structures, algorithm analysis, sorting, and graph algorithms.
Database Management Systems (CS301)
Relational algebra, SQL, normalization, and modern NoSQL databases.
Machine Learning Fundamentals (CS401)
Supervised/unsupervised learning, linear models, decision trees, and model evaluation.
Artificial Intelligence (CS402)
Search algorithms, knowledge representation, expert systems, and AI applications.
Computer Vision Lab (CS501L)
Hands-on implementation of image processing and CNN models using OpenCV and PyTorch.