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 fundamentals

• Multivariate Calculus
  Vector calculus, partial derivatives, gradient descent optimization, and applications to machine learning

• Discrete Mathematics
  Graph theory, combinatorics, mathematical logic, and algorithmic foundations

• Linear 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 frameworks

• Computer Vision
  Image processing, feature extraction, object detection, semantic segmentation, and classical CV algorithms

• Machine Learning
  Supervised learning, unsupervised learning and classical ML algorithms

• Digital Image Processing
  Image enhancement, filtering, morphological operations, and computer vision preprocessing techniques

• Foundation 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 optimization

• Virtualization and Cloud Computing
  Virtual machines, containerization (Docker), cloud platforms (AWS), serverless architecture, and distributed systems