To submit a proposed plan of study, use the Applied CS Program webform.
Courses - Core (26 credits) + Electives (6 credits)
Core
- CS 331 Introduction to Artificial Intelligence (4)
- CS 434 Machine Learning and Data Mining (4) or AI 534 Machine Learning (4)
- CS 475 Introduction to Parallel Programming (4)
- MTH 254 Vector Calculus I (4)
- MTH 341 Linear Algebra I (3)
- ST 314 Intro to Stats for Engineers (3)**
- ST 421 Introduction to Mathematical Statistics I (4)
Electives
- AI 530 Big Ideas in AI (3)
- AI 535 Deep Learning (4)
- CS 321 Introduction to Theory of Computation (3)
- CS 406 Projects (maximum 6 cr.)
- CS 420 Graph Theory with Applications to Computer Science (3)
- CS 440 Database Management Systems (4)
- CS 453 Scientific Visualization
- CS 458 Introduction to Information Visualization) (4)
- CS 492 Mobile Software Development (4)
- CS 493 Cloud Application Development (4)
- CS 499 Deep Learning (varies)
- CS 499 - If topic is appropriate (varies)
- MTH 342 Linear Algebra II (4)
- MTH 351 Intro to Numerical Analysis (3)
- MTH 451 Numerical Linear Algebra (3)
- MTH 463 Probability I (3)
- MTH 464 Probability II (3)
- MTH 465 Probability III (3)
- ROB 421 Applied Robotics (4)
- ROB 456 Intelligent Robotics (4)
- ST 422 Introduction to Mathematical Statistics II (4)
- ST 443 Applied Stochastic Models (3)
**ST 314 must be used in Focus area. Must complete 9 credits of CS restricted electives