Stackable Ones

One credit hour courses focusing on the three elements that can be “stacked” to create a custom data-science curriculum. These courses will be offered online too. Students can also satisfy the “Data Mind” requirement by taking existing courses in their major that cover any of the three elements. A full list of such courses will be announced soon. Additional opportunities are also being explored for undergraduate and graduate students who want to pursue more in-depth studies.

Machine Learning

We propose to develop a machine learning group in the College of Engineering. We believe that this is an urgent and critical move, for otherwise faculty and students will not see the presence of machine learning in the CoE. We have three objectives: 1) Create new graduate courses on machine learning; 2) Create and sustain a machine learning seminar series and a machine learning club that covers general machine learning topics at both undergraduate and graduate levels; and 3) Develop and maintain a group web page in machine learning in the CoE.

Development of two new graduate courses in Machine Learning is being funded by an Engineering Faculty seed grant to Stanley Chan and Ali el Gamal, assistant professors in Electrical and Computer Engineering. Machine Learning 1, which is being first taught in Spring 2019, includes modules that review fundamentals in statistics, and optimization, introduce classification methods, summarize python computing techniques. Machine Learning II will be taught in Fall 2019 and will review advanced statistics techniques, graphical models, and Big Data computing.

Current Undergraduate Courses

  • ECE 29595 - Introduction to Data Science
  • ECE 30010 - Introduction to Machine Learning and Pattern Recognition
  • ECE 30200 - Probabilistic Methods in Electrical and Computer Engineering
  • ECE 36800 - Data Structures
  • ECE 43800 - Digital Signal Processing with Applications
  • ECE 44000 - Transmission of Information
  • ECE 47300 - Introduction to Artificial Intelligence
  • AAE 36100 - Introduction to Random Variables in Engineering
  • ABE 20500 - Computations for Engineering Systems
  • ABE 30100 - Numerical and Computational Modeling in Biological Engineering
  • ABE 45000 - Finite Element Method In Design And Optimization
  • ABE 52700 - Computer Models In Environmental And Natural Resources Engineering
  • BME 40100 - Mathematical & Computational Analysis Of Complex System Dynamics In Biology, Medicine, & Healthcare
  • CE 40800 - Geographic Information Systems in Engineering
  • CHE 32000 - Statistical Modeling and Quality Enhancement
  • CHE 45000 - Design and Analysis of Processing Systems
  • IDE 36000 - Multidisciplinary Engineering Statistics
  • IE 23000 - Probability And Statistics In Engineering I
  • IE 33000 - Probability And Statistics In Engineering II
  • IE 33200 - Computing In Industrial Engineering
  • IE 33500 - Operations Research - Optimization
  • IE 33600 - Operations Research - Stochastic Models
  • IE 49000 - Statistical Learning
  • ME 36500 - Measurement And Control Systems I

FYE Learning Community

First-Year Engineering 131/132 has partnered with English and Libraries to offer a special section on Data Science-oriented FYE.”