Foundations of Prescriptive Analytics

Professor: Serdar Kadioglu | serdark@cs.brown.edu
Teaching Assistant: Max Heller| max_heller@brown.edu
Mailing Lists: {cs2951ostudent, cs2951oheadtas, cs2951otas}@lists.brown.edu
Class Hours: Fri 3pm - 5:20pm
Class Room: CIT 316
Office Hours: Serdar Fridays 1-2PM @ CIT 249 | Max Monday 2-4PM @ Zoom
Syllabus: Course Syllabus
EdStem: Course EdStem
Academic Code: Academic Honor Code

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Announcements
  • [Jan 28] Opening ceremony: January 28th, Friday 3pm - 5:20pm
  • [Past offerings] Course evaluations 2016/17, 2017/18, 2018/2019.

    Course Description

    We are undoubtedly in the middle of an Analytics Revolution that enabled turning huge amounts data into insights, and insights into predictions about the future. At the final frontier, Prescriptive Analytics aims to identify the best possible outcome given a certain objective function and a set of constraints. With that goal in mind, this course provides students with a comprehensive overview of the theory and practice of how to apply Prescriptive Analytics through optimization technology. A wide variety of state-of-the-art techniques are studied including: Boolean Satisfiability, Constraint Programming, Linear Programming, Integer Programming, Local Search Meta-Heuristics, and Large-Scale Optimization.

    The students are exposed to the industrially relevant software packages such as IBM Optimization Studio. The practical challenges encountered in implementing such systems are also explored. Additionally, the life-cycle of decision support systems is discussed and problems from real-life application domains such as planning, scheduling, resource allocation, supply-chain management, and logistics are addressed.

    Course Objectives

    The primary goal of this course is to introduce the fundamental ideas behind optimization technology to the extent that you can utilize this knowledge to build your own solvers based on various paradigms. Both complete and incomplete search methods, particularly tree-search and heuristic techniques will be covered in order to present different trade-offs. By the end of this course you will be able to transform a given optimization problem into analytical models with complementary strengths, and then, tackle it using off-the-shelf general purpose solvers and/or writing your own custom solutions. This course shall also complement descriptive and predictive analytics as it connects data-centric approaches with their optimum decision-making counterpart.

    Inclusive Course Goals

    To ensure that students are able to plan around conflicts and obligations without adversely impacting their grades, we aim to set deadlines that plan around student obligations as best we can and provide extensions when appropriate. To ensure that students can voice their own concerns about the course, we aim to hold sufficient office hours and make it clear to whom students can go and how to voice their concerns.