The Certificate in Data Mining and Machine Learning can be awarded in conjunction with any engineering master's degree. To qualify for this certificate, students enrolled in any master's in engineering program will need to meet the requirements listed below in addition to the standard requirements for their master's degree.
|CSE 417T||Introduction to Machine Learning||3|
|CSE 517A||Machine Learning||3|
|CSE 541T||Advanced Algorithms||3|
Choose two courses:
|CSE 511A||Introduction to Artificial Intelligence||3|
|CSE 513T||Theory of Artificial Intelligence and Machine Learning||3|
|CSE 514A||Data Mining||3|
|CSE 515T||Bayesian Methods in Machine Learning||3|
|CSE 519T||Advanced Machine Learning||3|
|Math 494||Mathematical Statistics||3|
Choose one course:
|CSE 427S||Cloud Computing with Big Data Applications||3|
|CSE 516A||Multi-Agent Systems||3|
|CSE 559A||Computer Vision||3|
|CSE 584A||Algorithms for Biosequence Comparison||3|
|CSE 587A||Algorithms for Computational Biology||3|
- All courses must be taken for a grade.
- Students with previous courses in machine learning may place out of CSE 417T. These students will be required to complete an additional foundations course for a total of three foundations courses.
- Students who began the certificate prior to FL16 who have successfully completed CSE 517A, independent of CSE 417T, will be required to complete an additional foundations course in place of CSE 417T for a total of three foundations courses. No student will be allowed to take CSE 417T after the successful completion of CSE 517A.
Any student who began the certificate prior to FL16 may choose to take CSE 441T in place of CSE 541T.