Machine Learning and Data Science Minor
The minor in Machine Learning and Data Science requires 8 courses: 2 core courses, 2 elective courses, and 4 courses from a specialization track.
At least 4 units of coursework must be unique to this minor program. These units cannot be applied to any other minor or certificate program, or the major requirements of any degree program. Such coursework may fulfill McCormick Social Sciences/Humanities (Theme), WCAS distribution requirements, or other unrestricted electives.
Course with a grade lower than “C-” cannot be applied to the minor.
Core courses (2 units):
| Course | Title |
|---|---|
| Programming Foundations | |
| COMP_SCI 150-0 | Fundamentals of Computer Programming 1.5 |
| Course | Title |
|---|---|
| Statistics Foundations (choose one) | |
| BMD_ENG 220-0 | Introduction to Biomedical Statistics |
| CHEM_ENG 312-0 | Probability and Statistics for Chemical Engineering |
| CIV_ENV 306-0 | Uncertainty Analysis |
| IEMS 201-0 | Introduction to Statistics |
| IEMS 303-0 | Statistics |
| MATH 314-0 | Probability and Statistics for Econometrics |
| STAT 320-2 | Statistical Theory & Methods 2 |
Specialization (4 units):
| Course | Title |
|---|---|
| Data Engineering Track | |
| COMP_SCI 217-0 | Data Management & Information Processing |
| or COMP_SCI 214-0 | Data Structures & Algorithms |
| IEMS 304-0 | Statistical Learning for Data Analysis |
| DATA_ENG 200-0 | Foundations of Data Science |
| DATA_ENG 300-0 | Data Engineering Studio |
| Course | Title |
|---|---|
| Machine Learning Track (not open to computer science majors/minors) | |
| COMP_SCI 111-0 | Fundamentals of Computer Programming |
| COMP_SCI 214-0 | Data Structures & Algorithms |
| COMP_SCI 348-0 | Introduction to Artificial Intelligence |
| COMP_SCI 349-0 | Machine Learning |
| Course | Title |
|---|---|
| Hybrid Track | |
| COMP_SCI 214-0 | Data Structures & Algorithms |
| COMP_SCI 349-0 | Machine Learning |
| DATA_ENG 200-0 | Foundations of Data Science |
| DATA_ENG 300-0 | Data Engineering Studio |
Elective Courses (2 units):
| Course | Title |
|---|---|
| BMD_ENG 311-0 | Computational Genomics |
| BMD_ENG 312-0 | Biomedical Applications in Machine Learning |
| BMD_ENG 313-0 | Wearable Devices: From Sensing to Biomedical Inference |
| CHEM_ENG 379-0 | Computational Biology: Analysis and Design of Living Systems |
| CIV_ENV 304-0 | Civil and Environmental Engineering Systems Analysis |
| CIV_ENV 377-0 | Choice Modelling in Engineering |
| CIV_ENV 395-0 | Special Topics in Civil and Environmental Engrg (Data Science for Urban Systems) |
| CIV_ENV 480-1 | Travel Demand Analysis & Forecasting 1 |
| CIV_ENV 480-2 | Advances in Travel Demand Analysis and Forecasting |
| CIV_ENV 495-0 | Selected Topics in Civil Engineering (Data Analytics for Transportation and Urban Infrastructure Applications) |
| COMP_SCI 312-0 | Data Privacy |
| COMP_SCI 332-0 | Online Markets |
| COMP_SCI 333-0 | Interactive Information Visualization |
| COMP_SCI 394-0 | Agile Software Development |
| COMP_SCI 396-0 | Special Topics in Computer Science (Computing, Ethics, and Society) or (Visualization for Scientific Communication) or (Modeling Relationships with Causal Inference) or (Natural and Artificial Vision) |
| COMP_SCI 397-0 | Special Projects in Computer Science (Rapid Prototyping for Software Innovation) or (Seminar in Statistical Language Modeling) |
| COMP_SCI 449-0 | Deep Learning |
| ELEC_ENG 328-0 | Information Theory & Learning |
| ELEC_ENG 335-0 | Deep Learning Foundations from Scratch |
| ELEC_ENG 373-0 | Deep Reinforcement Learning |
| ELEC_ENG 395-0 | Special Topics in Electrical Engineering (Optimization Techniques for Machine Learning and Deep Learning) |
| ELEC_ENG 424-0 | Distributed Optimization |
| ELEC_ENG 433-0 | Statistical Pattern Recognition |
| ES_APPM 345-0 | Applied Linear Algebra |
| ES_APPM 375-1 | Quantitative Biology I: Experiments, Data, Models, and Analysis |
| ES_APPM 375-2 | Quantitative Biology II: Experiments, Data, Models, and Analysis |
| ES_APPM 472-0 | Introduction to the Analysis of RNA Sequencing Data |
| ES_APPM 479-0 | Data Driven Methods for Dynamical Systems |
| IEMS 307-0 | Quality Improvement by Experimental Design |
| IEMS 308-0 | Data Science and Analytics |
| IEMS 313-0 | Foundations of Optimization |
| IEMS 340-0 | Qualitative Methods in Engineering Systems |
| IEMS 341-0 | Social Networks Analysis |
| IEMS 351-0 | Optimization Methods in Data Science |
| MAT_SCI 358-0 | Modeling and Simulation in Materials Science and Engineering |
| MAT_SCI 391-0 | |
| MECH_ENG 301-0 | Introduction to Robotics Laboratory |
| MECH_ENG 329-0 | Mechanistic Data Science for Engineering |
| MECH_ENG 341-0 | Computational Methods for Engineering Design |
| MECH_ENG 441-0 | Engineering Optimization for Product Design and Manufacturing |
| MECH_ENG 455-0 | Active Learning in Robotics |
| MECH_ENG 469-0 | Machine Learning and Artificial Intelligence for Robotics |
| MECH_ENG 495-0 | Selected Topics in Mechanical Engg (Sensory Navigation and Machine Learning for Robotics) |