Data Science and Artificial Intelligence, MSE

Curriculum

Core Requirements (4 cu's)

Linear Algebra or Convex Optimization

1. CIS 5150

   Fundamentals of Linear Algebra and Optimization

   or: MATH 5130 Computational Linear Algebra

   or: MATH 5140 Advanced Linear Algebra

   or: ESE 6050 Modern Convex Optimization

   or: STAT 5810 Convex Optimization for Statistics and Data Science
   1

Statistics

1. ESE 5420

   Statistics

   As published: “Statistics for Data Science”

   or: STAT 5110 Statistical Inference

   or: STAT 5120 Mathematical Statistics

   or: STAT 5350 Forecasting Methods for Management

   or: STAT 5420 Bayesian Methods and Computation
   1

Machine Learning

1. CIS 5190

   Applied Machine Learning

   or: CIS 5200 Machine Learning

   or: ESE 5460 Principles of Deep Learning
   1

Algorithms

1. CIS 5030

   Algorithms for Big Data

   or: CIS 5020 Analysis of Algorithms

   or: CIS 6770 Advanced Topics in Algorithms and Complexity
   1

Concentration (choose one)

Students are required to select one of the following tracks.

Data Science Concentration

1. CIS 5450

   Big Data Analytics
2. CIS 5500

   Database and Information Systems

   or: STAT 5710 Modern Data Mining

4 CUs of Electives (Choose courses from any of the elective buckets)

AI Concentration

1. CIS 5210

   Artificial Intelligence

   As published: “Artificial Intelligence”
   1
2. CIS 5810

   Computer Vision & Computational

   As published: “Computer Vision & Computational Photography”

   or: CIS 5300 Natural Language Processing

   or: CIS 6800 Advanced Topics in Machine Perception

   or: CIS 6300 Advanced Topics in Natural Language Processing

4 CU's of Electives (Two of which should come from the ML/multimodal AI bucket)

Elective Buckets Machine Learning, Multi-modal AI and Data Analysis

1. CIS 5210

   Artificial Intelligence

   As published: “Artificial Intelligence”
   1
2. CIS 5220

   Deep Learning for Data Science
   1
3. CIS 5270

   Trustworthy Machine Learning
4. CIS 5300

   Natural Language Processing
5. CIS 5450

   Big Data Analytics
6. CIS 5800

   Machine Perception
7. CIS 5810

   Computer Vision & Computational

   As published: “Computer Vision & Computational Photography”
8. CIS 6200

   Advanced Topics in Machine Learning
9. CIS 6250

   Theory of Machine Learning
10. CIS 6300

    Advanced Topics in Natural Language Processing
11. CIS 6800

    Advanced Topics in Machine Perception
    1
12. ESE 5140

    Graph Neural Networks
13. ESE 5380

    Machine Learning for Time-Series Data
14. ESE 5410

    Machine Learning for Data Science
15. ESE 5460

    Principles

    As published: “Principles of Deep Learning”
    1
16. ESE 6180

    Learning for Dynamics and Control
17. ESE 6450

    Deep Generative Models
    1
18. ESE 6500

    Learning in Robotics
19. MSE 5760

    Machine Learning and Its Applications in Materials Science
20. STAT 5710

    Modern Data Mining
    1

AI and Data Science for Discovery

1. BE 5040

   Biological Data Science II: Data Mining

   As published: “Biological Data Science II: Data Mining Principles for Epigenomics”
   1
2. BE 5060

   Introduction to Neuroengineering
3. BE 5210

   Brain-Computer Interfaces
4. BE 5660

   Networked Neuroscience
   0.5
5. BMIN 5030

   Data Science for Biomedical Informatics
   1
6. BMIN 5200

   Foundations of Artificial Intelligence in Health

   As published: “Foundations of Artificial Intelligence in Health”
7. BMIN 5210

   Advanced Methods and Health

   As published: “Advanced Methods and Health Applications in Machine Learning”
8. BMIN 5220

   Natural Language Processing for Health
9. BMIN 5490

   Exploring Data Science Methods with Health Care Data
10. CIS 5350

    Introduction to Bioinformatics
11. CIS 5360

    Fundamentals of Computational Biology
12. CIS 5370

    Biomedical Image Analysis
13. PHYS 5585

    Theoretical and Computational Neuroscience

Optimization, Systems and Control

1. ESE 5000

   Linear Systems Theory
2. ESE 5050

   Feedback Control Design and Analysis
   1
3. ESE 5060

   Introduction to Optimization Theory
4. ESE 6050

   Modern Convex Optimization
5. ESE 6060

   Combinatorial Optimization
6. ESE 6180

   Learning for Dynamics and Control
7. ESE 6190

   Model Predictive Control
   2

Social and Network Science

1. CIS 5230

   Ethical Algorithm Design
2. ECON 7300

   Econometrics I: Fundamentals
3. ECON 7310

   Econometrics II: Methods & Models
4. ECON 8310

   Econometrics III: Advanced Techniques of

   As published: “Econometrics III: Advanced Techniques of Cross-Section Econometrics”
   1
5. ECON 8320

   Econometrics IV: Advanced Techniques of Time-Series

   As published: “Econometrics IV: Advanced Techniques of Time-Series Econometrics”
6. MKTG 7760

   Applied Probability Models in Marketing

Surveys and Statistical Methods

1. MKTG 7120

   Data and Analysis for Marketing Decisions
2. OIDD 6120

   Business Analytics
3. STAT 5350

   Forecasting Methods for Management
4. STAT 6210

   Accelerated Regression Analysis for Business
5. STAT 7220

   Predictive Analytics for Business
   0.5
6. STAT 9200

   Sample Survey Methods
7. STAT 9210

   Observational Studies
8. STAT 9270

   Bayesian Statistical Theory and Methods
   1
9. STAT 9740

   Modern Regression for the Social

   As published: “Modern Regression for the Social, Behavioral and Biological Sciences”
   0.5

Data-Centric Programming

1. CIS 5050

   Software Systems
2. CIS 5500

   Database and Information Systems
3. CIS 5520

   Advanced Programming
4. CIS 5550

   Internet

   As published: “Internet and Web Systems”
   1
5. CIS 5590

   Programming and Problem Solving
6. CIS 5650

   GPU Programming and Architecture
7. CIS 5690

   GPU Computing for Machine Learning Systems
8. CIS 5730

   Software Engineering
9. CIS 6500

   Advanced Topics in Databases
10. CIT 5950

    Computer Systems Programming
11. ESE 5390

    Hardware/Software Co-Design for Machine

    As published: “Hardware/Software Co-Design for Machine Learning”

Robotics

1. ESE 6150

   RoboRacer Autonomous Racing Cars
2. ESE 6500

   Learning in Robotics
3. MEAM 5200

   Introduction to Robotics
4. MEAM 6200

   Advanced Robotics
   4

Simulation

1. CBE 5250

   Molecular Modeling and Simulations
2. CBE 5440

   Computational Science of Energy and

   As published: “Computational Science of Energy and Chemical Transformations”
3. CBE 5590

   Multiscale Modeling of Chemical and Biological Systems
4. MEAM 5270

   Finite Element Analysis
5. MEAM 5610

   Thermodynamics: Foundations, Energy, Materials
6. MEAM 6460

   Computational Mechanics

Mathematical and Algorithmic Foundations

1. AMCS 5141

   Advanced Linear Algebra
2. CIS 5020

   Analysis of Algorithms
3. CIS 5030

   Algorithms for Big Data
   1
4. CIS 6250

   Theory of Machine Learning
5. CIS 6770

   Advanced Topics in Algorithms and Complexity
6. CIT 5960

   Algorithms and Computation
   1
7. ENM 5020

   Numerical Methods and Modeling
8. ENM 5310

   Data-driven Modeling and Probabilistic Scientific Computing

   As published: “Data-driven Modeling and Probabilistic Scientific Computing”
9. ESE 5030

   Simulation Modeling and Analysis
10. ESE 5060

    Introduction to Optimization Theory
11. ESE 5450

    Data Mining: Learning from Massive Datasets
12. ESE 6050

    Modern Convex Optimization
13. ESE 6740

    Information Theory
    3
14. OIDD 9300

    Stochastic Models
15. STAT 5150

    Advanced Statistical Inference I
16. STAT 9270

    Bayesian Statistical Theory and Methods
    1

Other Electives

1. CIS 5120

   Introduction to Human Computer Interaction
2. CIS 7000

   Special Topics

   As published: “Special Topics Only relevant data science / AI topics upon approval”

Thesis / Practicum

1. DATS 5990

   Master's Independent Study

   As published: “Master's Independent Study (1 or 2 cu's of Practicum Total (1 cu per semester)) DATS Practicum and Thesis courses are not mandatory”
2. DATS 9990

   Master's Thesis

   As published: “Master's Thesis (2 cu's total needed (consecutive semesters)) DATS Practicum and Thesis courses are not mandatory”