Coursework

Principles and algorithms for mining large-scale data: clustering, association, classification, and anomaly detection.

Design and analysis of data structures: lists, trees, graphs, hashing, and complexity analysis.

Fundamentals of machine learning: supervised/unsupervised learning, neural networks, SVMs, decision trees, and applications.

Modern deep learning: neural networks, optimization, CNNs, RNNs, generative models, and practical projects.

Core data science principles: data wrangling, visualization, statistics, and machine learning in Python.

Introduction to programming and computer science using Python and Scheme. Functional, OOP, and declarative paradigms.

Convex optimization, algorithms, and applications in engineering and data science.

Theory and application of linear regression models, diagnostics, and inference.

Rigorous introduction to real analysis: limits, continuity, differentiation, integration.

Continuation of real analysis: sequences and series, multivariable calculus, advanced integration.

Study of curves, surfaces, and geometric transformations in the plane and space.

Numerical algorithms for solving mathematical problems: root finding, interpolation, numerical integration, and differential equations.