Undergraduate
MECH 490 – AI for Mechanical Engineering, Spring 2018
MNE 203 – Solid Mechanics I, Spring 2017
SDC 401 – Introduction to Mechatronics, Spring 2017
SDC 401 – Introduction to Mechatronics, Fall 2016
This course covers the basic control, instrumentation, and electrical systems. Various sensors and actuators with a microcontroller will be introduced and used for lab experiments. Based on such skills, basic theories of discrete signal processing and digital control will be applied to mechatronic systems. Arduino and Python will be intensively used for hands-on activities and two small class projects.
HSE 207 – Engineering Mechanics, Spring 2016
HSE 402 – Engineering Design Methods, Spring 2015
This course studies four essential ways of thinking for engineering problem solving: mathematical, computational, statistical, and optimal thinkings.
ESD 201 – Engineering Mechanics, Spring 2014
This course studies the essential and fundamental concepts of engineering mechanics including solid mechanics, dynamics, fluid dynamics. In this course, students are expected to understand basic knowledge of system physics in order to analyze and model mechanical systems.
ESD 301 – Engineering Drawing and Analysis, Spring 2014
This course not only provides the fundamental components of mechanical drawing, but also studies mechanical kinematics, system analysis and parameter optimization via simulation tools. In this course, students are expected to learn various computer simulation tools and their fundamentals for the design and development of mechanical products.
ESD 411 – Introduction to Vehicle Design, Fall 2013
This course will cover a broad range of topics for automotive design and engineering with the following selected areas:
– Automotive engineering (body and frame, power plant, power train, suspension system, steering system, brake system, electrical system)
– Car manufacturing processes
During this class, an automotive assembly plant visit will be made for hands-on experience. Furthermore, two guest lectures by automotive industry experts and one guest lecture by automotive design expert will be delivered.
graduate
HSE 545 – Machine Learning, Fall 2017
HSE 545 – Machine Learning, Spring 2016
DHE 570 – Advanced Multivariate Methods and Data Mining, Spring 2015
This course explores basic multivariate data analysis methods which will be an important tool for analyzing either simulation results or experimental data. Primacy will be given to the analysis of a set of data points. Students are expected to learn machine learning algorithms of data analytics and their implementations in Matlab.
DHE 571 – Advanced Control and Signal Processing, Fall 2014
This course deals with signals, systems, and transforms, from their theoretical mathematical foundations to practical implementation in computer algorithms. Furthermore, advanced linear feedback control with time-invariant linear systems will be covered.
DHE 802 – Special Topics in ESD 2 (Optimization Methods), Winter 2014
The course discusses fundamentals of discrete optimization methods as applied to engineering problems. Topics include discrete optimization models, integer and mixed-integer programming algorithms, graph search algorithms, heuristic algorithms, and case studies. Lectures present the key concepts and mathematical basis of each topic with its applications. The student are expected to learn how to create appropriate mathematical optimization models and to use analytical and computational techniques to solve them.
DHE 801 – Special Topics in ESD 1 (Big Data Analytics), Fall 2013
This course explores basic data analysis methods which will be an important tool for analyzing either simulation results or experimental data. Students are expected to learn algorithms of data analytics and their implementations in Matlab.
Details:
Programming in Matlab, Basic linear algebra, Statistics, Probability, Frequency domain analysis, Time series analysis, Principal component analysis, Multivariate classification (Logistic regression, SVM, SOM), Parameter estimation (MMSE, Maximum Likelihood, Bayes estimators), Neural networks, Bayesian networks, etc.