DATS 6103: Data Mining
This course introduces basic concepts of data mining as applied to Data Science, using Python as the programming language. Key algorithms covered include Linear/Logistic Regression (LR), Decision Trees (DT), Random Forests (RF), K-Nearest Neighbors (KNN), Support Vector Machines (SVM) and K-Means, with hands-on projects and real-world applications.
Math for Data Science
This course introduces fundamental mathematical concepts for Data Science. Key subjects covered include Set Theory, Linear Algebra, Calculus and Probability, with real-world interview questions.
Programming for Data Science
This course introduces fundamental programming concepts for Data Science. Key programming languages covered include Python, R and SQL, with real-world interview questions.
DATS 6450: Reinforcement Learning
This course introduces reinforcement learning through classical and deep architectures. Key algorithms covered include Monte Carlo (MC), Temporal Difference (TD), Value Function Approximation (VFA), Deep Q-Networks (DQN), Vanilla Policy Gradient (VPG), Proximal Policy Optimization (PPO), and Monte Carlo Tree Search (MCTS), with hands-on projects and real-world applications.