IE 598

Fall 2025 All Classes

All Classes

Special Topics

SHOW FINAL EXAM SCHEDULE

Credit: 1 TO 4 hours.

Subject offerings of new and developing areas of knowledge in industrial engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites.

Approved for Letter and S/U grading. May be repeated in the same or separate terms if topics vary.

My Favorites

My Schedule

Section Status updates every 10 minutes.

IE 598 class schedule data for fall 2025
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
79961	Lecture-Discussion	SC	9:00AM -11:50AM	S	4039 Campus Instructional Facility	Prasad, I	Part of Term: B Date Range: 10/20/25-12/10/25 Credit: 2 hours Section Title: Structured Credit II Section Info: Asset-backed financing is important to understand because it illustrates a fundamental aspect of how financial markets and lending work in the economy. By securitizing assets like mortgages, auto loans, or student loans, financial institutions can create investment opportunities that provide liquidity and lower borrowing costs for borrowers. This process not only facilitates access to funds for individuals and businesses but also spreads risk across a wider pool of investors. Grasping the concept of asset-backed financing helps students understand the mechanisms behind loans and fixed income investments, highlighting how diverse financial instruments can be used to allocate capital efficiently and support economic growth. We will especially focus on the mortgage-backed securities market, loan performance analytics and investment thesis considered by professionals. The course introduces aspects of the mortgage and MBS markets, prepayment and default modeling, structuring of RMBS bonds, and more. Restriction(s): Restricted to Financial Engineering major(s).
61631	Lecture-Discussion	YX	3:30PM -4:50PM	TR	206 Transportation Building	Xu, Y	Part of Term: 1 Date Range: 08/25/25-12/10/25 Credit: 4 hours Section Title: Foundations of Modern ML Section Info: Prerequisite: MATH 257, MATH 415, or equivalent course on linear algebra and MATH 362, MATH 461, or equivalent course on probability. Course Description: This course covers foundational topics in theory of machine learning for modern use, including statistical, computational, and causal considerations in large-scale and online scenarios. We start with the classical framework of statistical learning theory and a basic probability and optimization toolkit required for understanding machine learning. We then explore two different frameworks, online/bandit learning and causal inference, highlighting the unique challenges therein and key algorithmic principles to address them. Finally, we review recent progress in understanding the mysteries of deep learning. The curriculum covers both foundational theories and cutting-edge research, emphasizing important connections to statistics, probability, optimization, dynamical systems, and game theory Restriction(s): Restricted to Graduate - Urbana-Champaign.
60476	Lecture-Discussion	YYL	2:00PM -3:20PM	TR	137 Loomis Laboratory	Li, Y	Part of Term: 1 Date Range: 08/25/25-12/10/25 Credit: 4 hours Section Title: Reinforcement Learning and Lea Section Info: Course description: Reinforcement learning (RL) and learning-based control (LBC) are two closely connected fields, both of which focus on decision-making and control of uncertain dynamical systems, also known as Markov decision processes. This course will discuss the similarities, differences, and interconnections of RL and LBC from the aspects of algorithm design, theoretical tools, and real-world applications. In particular, the course will start with dynamic programming as a common tool for RL and LBC, then diving into different algorithm design philosophies of RL and LBC, e.g., model-free approaches such as Q learning, actor-critic, policy gradient for RL, and model-based approaches such as system identification, certainty equivalence, and model predictive control for LBC. The course will mostly focus on tabular cases and unconstrained cases. Function approximations and safety RL/LBC will also be discussed if time permits. Prerequisite: MATH 257, MATH 415, or equivalent course on linear algebra and MATH 362, MATH 461, or equivalent course on probability. Previous knowledge of random processes, machine learning, and/or control systems is strongly recommended. Restriction(s): Restricted to Graduate - Urbana-Champaign.