IE 598

Fall 2024 All Classes

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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.

1 to 4 graduate hours. No professional credit. Approved for Letter and S/U grading. May be repeated in the same or separate terms if topics vary.

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IE 598 class schedule data for fall 2024
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
65360	Lecture-Discussion	CDM	2:00PM -3:20PM	TR	4101 Materials Science & Eng Bld	Ray Chaudhury, B	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Collective Decision Making Section Info: Prerequisites: CS 101, IE 300, and IE 411 or equivalent courses. This course focuses on decision-making in the presence of multiple agents with preferences. This would cover topics on optimized democracy (voting rules, axioms, liquid democracy), embedded ethics (biases in algorithms, societal effects of algorithms, algorithmic fairness), similar paradigms in ML (federated learning), and related topics from economics (fair division, general equilibrium theory, and stable matching). The course is highly-interdisciplinary, and the main takeaway is to familiarize students with a class of problems that are of interest to operations research, computer science, economics, and law, and how the combined literature can help us design efficient solutions.
79961	Lecture-Discussion Lecture-Discussion	SC SC	5:00PM -6:20PM 6:00PM -7:20PM	F M	1310 Digital Computer Laboratory 1310 Digital Computer Laboratory	Prasad, I Prasad, I	Part of Term: A Date Range: 08/26/24-10/18/24 Credit: 2 hours Section Title: Structured Credit 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: 08/26/24-12/11/24 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 will discuss the algorithm design, theoretical analysis, and simulations of dynamic programming (DP) and reinforcement learning (RL) in either finite horizon or infinite horizon, with either full observations or partial observations. Most discussions will focus on the tabular case. DP and RL with function approximations will also be introduced if time permits. 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.