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 List
| Course |
Title |
| Fundamentals of Programming and Computational Problem Solving |
| Programming for Engineers |
| Honors Programming for Engineers |
Course List
| Course |
Title |
| Probability and Statistics for Chemical Engineering |
| Uncertainty Analysis |
| Probability and Statistics for Engineers |
| Introduction to Statistics |
| Engineering Statistics |
| Probability and Statistics for Econometrics |
| Statistical Theory & Methods 2 |
Specialization (4 units):
Course List
| Course |
Title |
| Data Structures & Algorithms (Formerly Comp_Sci 214) |
| Applied Data Management |
| Statistical Learning for Data Analysis |
| Introduction to Statistical Learning |
| Foundations of Data Science |
| Data Engineering Studio |
Course List
| Course |
Title |
| Software Design & Composition |
| Fundamentals of Computer Programming |
| Data Structures & Algorithms (Formerly Comp_Sci 214) |
| Introduction to Artificial Intelligence |
| Machine Learning |
Course List
| Course |
Title |
| Data Structures & Algorithms (Formerly Comp_Sci 214) |
| Machine Learning |
| Foundations of Data Science |
| Data Engineering Studio |
Elective Courses (2 units):
Course List
| Course |
Title |
| Computational Genomics |
| Biomedical Applications in Machine Learning |
| Wearable Devices: From Sensing to Biomedical Inference |
| Computational Biology: Analysis and Design of Living Systems |
| Civil and Environmental Engineering Systems Analysis |
| Data Science for Urban Systems |
| Choice Modelling in Engineering |
| Special Topics in Civil and Environmental Engrg (Data Science for Urban Systems) |
| Data Analytics for Urban Systems |
| Travel Demand Analysis & Forecasting 1 |
| Advances in Travel Demand Analysis and Forecasting |
| Selected Topics in Civil Engineering (Data Analytics for Transportation and Urban Infrastructure Applications) |
| Introduction to Robotics Laboratory |
| Introduction to Robotics Laboratory |
| Data Privacy |
| Data Privacy |
| Online Markets |
| Interactive Information Visualization |
| Natural & Artificial Vision |
| Causal Graphical Models |
| Rapid Prototyping for Software Innovation |
| Agile Software Development |
| Special Topics in Computer Science (Computing, Ethics, and Society) |
| Special Projects in Computer Science (Seminar in Statistical Language Modeling) |
| Deep Learning |
| Machine Learning & Artificial Intelligence for Robotics |
| Machine Learning and Artificial Intelligence for Robotics |
| Special Topics in Computer Science (Visualization for Scientific Communication) |
| Data Engineering Studio |
| Information Theory & Learning |
| Deep Learning Foundations from Scratch |
| Deep Reinforcement Learning |
| Special Topics in Electrical Engineering (Optimization Techniques for Machine Learning and Deep Learning) |
| Distributed Optimization |
| Statistical Pattern Recognition |
| Applied Linear Algebra |
| Quantitative Biology I: Experiments, Data, Models, and Analysis |
| Quantitative Biology II: Experiments, Data, Models, and Analysis |
| Introduction to the Analysis of RNA Sequencing Data |
| Data Driven Methods for Dynamical Systems |
| Foundations of Modern Machine Learning |
| Quality Improvement by Experimental Design |
| Foundations of Optimization |
| Qualitative Methods in Engineering Systems |
| Social Networks Analysis |
| Social Networks Analysis |
| Social Networks Analysis |
| Social Networks Analysis |
| Social Network Analysis |
| Social Network Analysis |
| AI-Enabled Sequential Decision Making |
| Modeling and Simulation in Materials Science and Engineering |
| Process and Experimental Design |
| Mechanistic Data Science for Engineering |
| Computational Methods for Engineering Design |
| Engineering Optimization for Product Design and Manufacturing |
| Active Learning in Robotics |
| Selected Topics in Mechanical Engg (Sensory Navigation and Machine Learning for Robotics) |