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 program within the McKelvey School of Engineering will need to meet the requirements listed below in addition to the standard requirements for their master's degree. All courses must be taken for a grade. Courses must be graded at a D– or better in order to count toward the certificate.

Required Courses

CSE 4107Introduction to Machine Learning3
or ESE 4170 Introduction to Machine Learning and Pattern Classification
CSE 5107Machine Learning3
CSE 5401Advanced Algorithms3
Total Units9

Foundations Courses

Students select two of the following courses:

CSE 4102Introduction to Artificial Intelligence3
CSE 5100Deep Reinforcement Learning3
CSE 5103Theory of Artificial Intelligence and Machine Learning3
CSE 5104Data Mining3
CSE 5105Bayesian Methods in Machine Learning3
CSE 5109Advanced Machine Learning3
CSE 5403Algorithms for Nonlinear Optimization3
CSE 5610Large Language Models3
ESE 4150Optimization3
SDS 4010Probability3
or ESE 5200 Probability and Stochastic Processes
SDS 4020Mathematical Statistics3

Domain Courses

Students choose one of the following courses:

CSE 4207Cloud Computing With Big Data Applications3
CSE 5106Multi-Agent Systems3
CSE 5108Human-In-The-Loop Computation3
CSE 5270Natural Language Processing3
CSE 5310AI for Health3
CSE 5370Trustworthy Autonomy3
CSE 5505Adversarial AI3
CSE 5507Advanced Visualization3
CSE 5509Computer Vision3
CSE 5804Algorithms for Biosequence Comparison3
CSE 5807Algorithms for Computational Biology3
ESE 5130Large-Scale Optimization for Data Science3

Additional Information

Students with previous courses in machine learning may place out of CSE 4107 Introduction to Machine Learning. These students will be required to complete an additional foundations course for a total of three foundations courses.

Contact Info