Additional MATH courses for Summer 2020

The Department of Applied Mathematics have added many courses this summer, including required courses for many majors, and an exciting new “I Course” (IPRO substitute). Course are listed below with instructors, followed by descriptions.
MATH 151 – Calculus I – George Zazi
MATH 152 – Calculus II – Dave Maslanka
MATH 180 – Fundamentals of Discrete Math – for ITM majors – Aleksey Zelenberg
MATH 230 – Intro. Discrete Math – CS 330 alternative – Instructor TBD
MATH 251 – Multivariate Calculus – Sara (Sharzad) or Aleksey Zelenberg TBD
MATH 252 – Intro. Diff Eq – Kiah Wah Ong
MATH 332 – Elem Linear Algebra – Kiah Wah Ong
MATH 333 – Matrix Algebra and Complex Variables – Rohan Attele
MATH 474 – Probability and Statistics – Art Lubin
MATH 475 – Probability – Rob Ellis
MATH 497 – Problem-Solving Projects in Business, Industry, and Government – counts for IPRO credit – Rob Ellis and David Eads
SCI 497-109 – Intro to Statistical Learning and Big Data – counts as a MATH elective – Aleksey Zelenberg
SCI 497-111 – An Overview to Machine Learning – counts as a MATH elective – Yuhan Ding
SCI 497-112 – An Introduction to Neural Networks – counts as a MATH elective – Sara (Shahrzad) Zelenberg
MATH 597-01 – Deep Learning for Science and Engineering – Matthew Dixon

Summer 2020 Math 497 (I-Course) Problem-Solving Projects in Business, Industry, and Government (Eads/Ellis)
  • IPRO credit/I-Course status pending approval as of April 10
  • WORK in a team — diverse skills and majors build the whole
  • LEARN from and MENTOR your peers
  • APPLY data science, mathematics, statistics to real-world problems
  • CREATE and PROMOTE a paper, poster, talk, GitHub repo, app, dashboard
  • BUILD marketable skills for employment or graduate school
Co-taught by:
Example Topics (adoption or modification pending final selection)

Topic 1: Demographic distribution and effects of the COVID-19 pandemic in Chicago

Topic 2: Polling location placement. Determine if poling places in the counties/precincts of a state are fairly located. Red/Blue team format: Blue team optimizes placement, Red team analyzes for weaknesses.

Topic 3: Facebook political advertising data sets. Explore patterns and targeting of Facebook political ads since May 2018. A Facebook and a ProPublica data set are available.

Format: Directed team research and problem-solving with a faculty adviser and subject matter expert; twice-weekly class meetings; plus individual and team work each week. Periodic deliverables will lead to a final paper/poster/talk, and IPRO Day participation.


MATH 597 – Deep Learning for Science and Engineering
Instructor: Matthew Dixon

Course description: Deep Neural Networks for science and engineering is a research course on designing and implementing solutions to challenging mathematical and statistical problems arising in modeling and prediction. Students shall form groups and solve a specific scientific or engineering problem, using deep neural networks.

Course Objectives: On completion of the course, the student should expect to demonstrate expertise in the following topics:

  • Review of statistical learning theory
  • Feedforward architectures
  • Training and back-propagation
  • Error analysis of feedforward architecture
  • Surrogate modeling for PDEs, including applications in calibration and enforcing conservation laws
  • Recurrent neural networks for modeling dynamical systems
  • Convolution neural networks for spatio-temporal modeling
  • Autoencoders for dimensionality reduction

Other courses: