Relevant Coursework
This page highlights key courses that have shaped my technical foundation in Computer Science, with emphasis on Machine Learning, Computer Vision, and Software Engineering.
Mathematics
Foundation Courses
Introduction to Probability and Statistics
Statistical inference, probability distributions, hypothesis testing, and data analysis fundamentalsMultivariate Calculus
Vector calculus, partial derivatives, gradient descent optimization, and applications to machine learningDiscrete Mathematics
Graph theory, combinatorics, mathematical logic, and algorithmic foundationsLinear Algebra
Vector spaces, matrix operations, eigenvalues and eigenvectors, and applications to machine learning
Artificial Intelligence & Machine Learning
Core ML Courses
Deep Learning
Neural network architectures, backpropagation, CNNs, RNNs, transformers, generative models, and modern deep learning frameworksComputer Vision
Image processing, feature extraction, object detection, semantic segmentation, and classical CV algorithmsMachine Learning
Supervised learning, unsupervised learning and classical ML algorithmsDigital Image Processing
Image enhancement, filtering, morphological operations, and computer vision preprocessing techniquesFoundation of Data Science
Statistical modeling, data wrangling, exploratory data analysis, and machine learning pipelines
Systems & Engineering
Software Engineering
Data Structures
Arrays, linked lists, trees, graphs, hash tables, algorithm complexity analysis, and optimizationVirtualization and Cloud Computing
Virtual machines, containerization (Docker), cloud platforms (AWS), serverless architecture, and distributed systems
