Curriculum: Electives

In addition to meeting the five core area requirements, each student is required to complete five elective courses. These courses are required for getting a complete breadth in ML. These courses must be chosen from at least two of the five subject areas listed below. In addition, students can use up to six special problems research hours to satisfy this requirement. 

i. Statistics and Applied Probability: To build breadth and depth in the areas of statistics and probability as applied to ML.

  • AE 6505, Kalman Filtering

  • BMED 6700, Biostatistics

  • ECE 6558, Stochastic Systems

  • ECE 6601, Random Processes

  • ECE 6605, Information Theory

  • ISYE 6404, Nonparametric Data Analysis

  • ISYE 6413, Design and Analysis of Experiments

  • ISYE 6414, Regression Analysis

  • ISYE 6416, Computational Statistics

  • ISYE 6420, Bayesian Statistics

  • ISYE 6761, Stochastic Processes I

  • ISYE 6762, Stochastic Processes II

  • ISYE 7400, Adv Design-Experiments

  • ISYE 7401, Adv Statistical Modeling

  • ISYE 7405, Multivariate Data Analysis

  • MATH 6263, Testing Statistical Hypotheses

  • MATH 6266, Statistical Linear Modeling

  • MATH 6267, Multivariate Statistical Analysis

  • MATH 7244, Stochastic Processes and Stochastic Calculus I

  • MATH 7245, Stochastic Processes and Stochastic Calculus II

 

ii. Advanced Theory: To build a deeper understanding of foundations of ML.

  • CS 7280, Network Science

  • CS 7510, Graph Algorithms

  • CS 7520, Approximation Algorithms

  • CS 7530, Randomized Algorithms

  • CS 7535, Markov Chain Monte Carlo Algorithms

  • CS 7540, Spectral Algorithms

  • CS 7545, Machine Learning Theory

  • ECE 6283, Harmonic Analysis and Signal Processing

  • ECE 6555, Linear Estimation

  • ISYE 7682, Convexity

  • MATH 6112, Advanced Linear Algebra

  • MATH 6221, Advanced Classical Probability Theory

  • MATH 6580, Introduction to Hilbert Space

  • MATH 7338, Functional Analysis

  • MATH 7586, Tensor Analysis

  • MATH 88XX, Special Topics: Mathematical Foundations of Learning Theory

  • MATH 88XX, Special Topics: High Dimensional Probability and Statistics

 

iii. Applications: To develop a breadth and depth in variety of applications domains impacted by/with ML.

  • AE 6373, Advanced Design Methods

  • AE 8803, Machine Learning for Control Systems

  • AE 8803, Nonlinear Stochastic Optimal Control

  • BMED 6780, Medical Image Processing

  • BMED 8813BHI, Biomedical and Health Informatics

  • BMED 8813MHI, mHealth Informatics

  • BMED 8813MLB, Machine Learning in Biomedicine

  • BMED 8823ALG, OMICS Data and Bioinformatics Algorithms

  • CS 6440, Introduction to Health Informatics

  • CS 6465, Computational Journalism

  • CS 6474, Social Computing

  • CS 6475, Computational Photography

  • CS 6476, Computer Vision

  • CS 6601, Artificial Intelligence

  • CS 7450, Information Visualization

  • CS 7476, Advanced Computer Vision

  • CS 7630, Autonomous Robots

  • CS 7636, Computational Perception

  • CS 7646, Machine Learning for Trading

  • CS 7650, Natural Language Processing

  • CSE 6141, Massive Graph Analysis

  • CSE 6240, Web Search and Text Mining

  • CSE 6242, Data and Visual Analytics

  • CSE 6301, Algorithms in Bioinformatics and Computational Biology

  • ECE 4580, Computational Computer Vision

  • ECE 6255, Digital Processing of Speech Signals

  • ECE 6258, Digital Image Processing

  • ECE 6260, Data Compression and Modeling

  • ECE 6273, Methods of Pattern Recognition with Application to Voice

  • ECE 6550, Linear Systems and Controls

  • ECE 8813, Network Security

  • ISYE 6421, Biostatistics

  • ISYE 6810, Systems Monitoring and Prognosis

  • ISYE 7201, Production Systems

  • ISYE 7204 Info Prod & Ser Sys

  • ISYE 7203, Logistics Systems

  • HS 6000, Healthcare Delivery

  • MATH 6759, Stochastic Processes in Finance

  • MATH 6783, Financial Data Analysis

 

iv. Computing and Optimization: To provide more breadth and foundation in areas of math, optimization and computation for ML.

  • CS 6505, Computability and Algorithms

  • CS 6550, Design and Analysis of Algorithms

  • CSE 6140, Computational Science and Engineering Algorithms

  • CSE 6643, Numerical Linear Algebra

  • CSE 6644, Iterative Methods for Systems of Equations

  • CSE 6710, Numerical Methods I

  • CSE 6711, Numerical Methods II

  • ISYE 6645, Monte Carlo Methods

  • ISYE 6662, Discrete Optimization

  • ISYE 6664, Stochastic Optimization

  • ISYE 6679, Computational methods for optimization

  • ISYE 7686, Advanced Combinatorial Optimization

  • ISYE 7687, Advanced Integer Programming

 

v. Platforms: To provide breadth and depth in computing platforms that support ML and Computation.

  • CS 6421, Temporal, Spatial, and Active Databases

  • CS 6430, Parallel and Distributed Databases

  • CS 6290, High-Performance Computer Architecture

  • CSE 6220, High Performance Computing

  • CSE 6230, High Performance Parallel Computing