Electives
All courses on this page are pre-approved. It’s your responsibility to find out when a course is offered and make sure you have the necessary prerequisites and preparation to take a course. For other 400 and 500 level courses not listed below, you’ll need approval from the MSFE program.
Not every course is offered every semester. It’s important that you plan ahead and learn in what semesters your courses of interest are typically offered. To see in what semesters a course has been offered historically, visit http://catalog.illinois.edu/courses-of-instruction/, click the corresponding department and then the course title. It’ll show all semesters this course has been offered. E.g., for IE 525, it shows
To see whether a particular course is offered during an upcoming semester, visit https://courses.illinois.edu/schedule, click the year, then the semester, then the department.
MSFE students are afforded considerable flexibility in choosing electives within the program and have an additional option of obtaining a concentration within the MSFE program.
Electives must be at the 400 or 500 level to meet graduation requirements. The elective courses must also be relevant to the Financial Engineering curriculum with approval from the department. Students have the option to choose from electives with MSFE priority designation or other popular electives across the campus.
For a complete list of the official MSFE Program Requirements, please refer to the Academic Catalog.
| Electives with MSFE priority designationd | |
| Course title and description | Credit Hours |
|
IE 421 Special Topics - High Frequency Trading
|
4
|
|
IE 517 Machine Learning in Finance Lab
|
2 |
|
IE 524 Optimization in Finance Section B (2nd half of semester)
|
2
|
|
IE 598 Special Topics - Computer Science for Quants
|
1
|
|
FIN 517 Advanced Term Structure Models (2nd half of semester)
|
2
|
|
FIN 554 Algorithmic Trading Systems Design & Testing
|
4
|
|
FIN 566 Algorithmic Market Microstructure
|
4
|
|
FIN 580 Special Topics in Finance - Option Trading Market Making
|
4
|
d These courses are taught by practitioners and industry experts. MSFE students are given priority registration for these courses.
| Other Popular Campus Electives |
| ACE 427 Commodity Price Analysis |
|
ACE 520 Time Series Econometrics for Price Analysis
|
|
ACE 527 Advanced Price Analysis
|
|
ACE 528 Research in Futures Markets
|
|
BDI 411 Blockchain, Tokens, and Their Application in Business
|
|
CS 412 Introduction to Data Mining
|
|
CS 440 Artificial Intelligence
|
|
CS 441 Applied Machine Learning
|
|
CS 444 Deep Learning for Computer Vision
|
|
CS 446 Machine Learning
|
|
CS 447 Natural Language Processing
|
|
CS 450 Numerical Analysis
|
|
CS 473 Algorithms
|
|
CS 484 Parallel Programming
|
|
CS 512 Data Mining Principles
|
|
CS 542 Statistical Reinforcement Learning
|
|
CS 545 Machine Learning for Signal Processing
|
|
CS 555 Numerical Methods for PDEs
|
|
ECE 534 Random Processes
|
|
ECE 590 Arch./Compilers/Parallel Comp (Special Topics)
|
|
FIN 501 Economics of Stock Market Fundamentals
|
|
FIN 504 Accounting for Financial Analysis
|
|
FIN 511 Investments
|
|
FIN 515 Fixed Income Portfolios
|
|
FIN 518 Financial Modeling
|
|
FIN 526 Investment Banking
|
|
FIN 527 Mergers & Acquisitions Topics
|
|
FIN 529 Applied Financial Analysis
|
|
FIN 551 International Finance
|
|
FIN 572 The Finance of Mergers and Acquisitions
|
|
FIN 589 Applied Portfolio Management
|
|
IE 405 Computing for ISE (Algorithm Design, C++, SQL)
|
|
IE 434 Deep Learning Math and Applications
|
|
IE 518 Queuing Systems
|
|
IE 598 Special Topics: Collective Decision Making
|
|
IS 457 Data Storytelling
|
|
MATH 561 Theory of Probability I
|
|
MATH 564 Applied Stochastic Processes
|
|
STAT 425 Statistical Modeling I
|
|
STAT 429 Time Series Analysis
|
|
STAT 430 Machine Learning w/ Time Series
|
|
STAT 431 Applied Bayesian Analysis
|
|
STAT 432 Basics of Statistical Learning
|
|
STAT 437 Unsupervised Learning
|
|
STAT 447 Data Science Programming Methods
|
|
STAT 448 Advanced Data Analysis
|
|
STAT 480 Data Science Foundations
|
|
STAT 525 Computational Statistics
|
|
STAT 542 Statistical Learning
|
|
STAT 556 Advanced Time Series Analysis
|
eOther courses may be considered and requested of the program director.