Advancing Data & AI
The MSE in Data Science & Artificial Intelligence (DSAI) brings together two rapidly evolving fields to prepare students to build, analyze and responsibly apply intelligent systems across disciplines. Students build a rigorous foundation in statistics, machine learning and large scale data systems, then apply it to real world problems in areas such as healthcare, finance, robotics and public policy. Coursework pairs technical depth with an emphasis on responsible deployment, including fairness, interpretability and societal impact. Through hands on projects and research, graduates leave ready not just to use today’s data science and AI tools, but to build the next generation of them.
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CORE (4 CUs): 1. Linear Algebra or Convex Optimization: CIS 5150: Linear Algebra 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 2. Statistics: ESE 5420 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 3. Machine Learning: CIS 5190: Introduction to Machine Learning OR CIS 5200: Machine Learning OR ESE 5460: Principles of Deep Learning 4. Algorithms: CIS 5030: Algorithms for Big Data OR CIS 5020: Algorithms OR CIS 6770: Advanced Topics in Algorithms and Complexity |
Concentrations
Artificial Intelligence Concentration
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1. CIS 5210: Artificial Intelligence 2. CIS 5810: Computer Vision OR CIS 5300: Natural Language Processing OR CIS 6800 Advanced Topics in Machine Perception OR CIS 6300 Advanced Topics in Natural Language Processing 4 CUs of electives, two of which should come from the ML/multimodal AI bucket. |
Data Science Concentration
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1. CIS 5450: Big Data Analytics 2. CIS 5500: Databases OR STAT 5710: Modern Data Mining 4 CUs of electives from any bucket |
Elective Buckets
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● CIS 5210 Artificial Intelligence ● CIS 5220: Deep Learning for Data Science ● CIS 5300: Natural Language Processing ● CIS 5800: Machine Perception ● CIS 5810: Computer Vision ● CIS 6200: Advanced Topics in Machine Learning ● CIS 6250: Computational Learning Theory ● CIS 6300: Advanced Topics in Natural Language Processing ● CIS 6800: Advanced Topics in Machine Perception ● CIS 5270: Trustworthy Machine Learning ● ESE 5140: Graph Neural Networks ● ESE 5380: Machine Learning for Time-Series Data ● ESE 5410: Machine Learning for Data Science ● ESE 5460: Principles of Deep Learning ● ESE 6180: Learning for Dynamics and Control ● ESE 6450: Deep Generative Models ● ESE 6500: Learning in Robotics ● MSE 5760: Machine Learning and its Applications in Materials Science ● STAT 5710: Modern Data Mining ● CIS 5450: Big Data Analytics |
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● BE 5210: Brain-Computer Interfaces ● BE 5600: Network Neuroscience ● BE/CIS xxxx: Generative Models for Medicine [Chatterjee] ● BE/CIS xxxx: Machine Learning for Bioscience [Dyer] ● CIS 5350: Introduction to Bioinformatics ● CIS 5360: Fundamentals of Computational Biology ● CIS 5370: Biomedical Image Analysis ● PHYS 5850: Theoretical and Computational Neuroscience ● BE 5060: Introduction to Neuroengineering ● BE 5040: Biological Data Science II ● BE 5670: Mathematical Computation Methods for Modeling Biological Systems ● BMIN 5030: Data Science for Biomedical Informatics ● BMIN 5200: Foundations of AI in Health ● BMIN 5210: Advanced Methods and Health Applications in ML ● BMIN 5220: NLP for Health ● BMIN 5490: Exploring Data Science Methods with Health Care Data |
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● ESE 5000: Linear Systems Theory ● ESE 5050: Control Systems ● ESE 5060: Linear Optimization ● ESE 6050: Modern Convex Optimization ● ESE 6060: Combinatorial Optimization ● ESE 6190: Model Predictive Control ● ESE 6180: Learning for Dynamics and Control |
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● CIS 5230: Ethical Algorithm Design ● ECON 7050: Econometrics I – Fundamentals ● ECON 7210: Econometrics III – Advanced Techniques of Cross-Section Econometrics ● ECON 7220: Econometrics IV – Advanced Techniques of Time-Series Econometrics ● MKTG 7760: Applied Probability Models in Marketing |
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● MKTG 7120: Data and Analysis for Marketing Decisions ● OIDD 6120: Business Analytics (limited to MBA students only) ● STAT 5350: Forecasting Methods for Management ● STAT 6210: Accelerated Regression Analysis (limited to MBA students only) ● STAT 7220: Predictive Analytics for Business ● STAT 9200: Sample Survey Methods ● STAT 9210: Observational Studies ● STAT 9270 Bayesian Statistics ● STAT 9740: Modern Regression for the Social, Behavioral, and Biological Sciences |
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● CIS 5050: Software Systems ● CIS 5500: Databases ● CIS 5520: Advanced Programming ● CIS 5550: Internet and Web Systems ● CIS 5590: Programming and Problem Solving ● CIS 5730: Software Engineering ● CIT 5950: Computer Systems Programming ● CIS 5650: GPU Programming and Architecture ● ESE 5390: Hardware/Software Co-Design for Machine Learning ● CIS 5690: GPU Computing for Machine Learning Systems ● CIS 6500: Advanced Topics in Databases |
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● MEAM 5200: Robotics ● MEAM 6200: Advanced Robotics ● ESE 6500: Learning in Robotics ● ESE 6150: F1/10 Autonomous Racing Cars |
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● CBE 5250: Molecular Modeling and Simulations ● CBE 5440: Computational Science of Energy and Chemical Transformations ● CBE 5590: Multi-Modeling of Biological Systems ● MEAM 5270: Finite Element Analysis ● MEAM 6460: Computational Mechanics ● MSE 5610: Atomic Modeling in Materials Science |
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● AMCS 5141: Advanced Linear Algebra ● CIS 5020: Algorithms ● CIS 5030 Algorithms for Big Data ● CIS 6250: Theory of Machine Learning ● CIS 6770: Advanced Topics in Algorithms and Complexity ● CIT 5960: Algorithms & Computation ● ENM 5020: Numerical Methods ● ENM 5310: Data-driven Modeling and Probabilistic Scientific Computing ● ESE 5060: Introduction to Optimization Theory ● ESE 5450: Data Mining – Learning from Massive Datasets ● ESE 5030: Simulation Modeling and Analysis ● ESE 6050: Modern Convex Optimization ● ESE 6740: Information Theory ● OIDD 9300: Stochastic Models ● STAT 5150: Statistical Methods 1 ● STAT 9270: Bayesian Statistical Theory and Methods |
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● CIS 5120: Human-Computer Interaction ● CIS 7000 Courses: Only relevant data science / AI topics upon approval |
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