Foundations of Prescriptive Analytics
- Ugur Cetintemel
- Serdar Kadioglu
- Course Home Page:
|Meeting Time:||P: Tue 4:00-6:20|
|Exam Group:||16: 05/17/2017 at 2:00 P.M.|
|Offered this year?||Yes|
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 its final frontiers, Prescriptive Analytics is aimed at identifying the best possible action to take given the constraints and the objective. To that end, 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 is 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.
This course requires good programming skills and understanding of basic algorithms and data structures (e.g., stacks, queues etc.). Prior knowledge of linear algebra and matrix calculation can help but it is not required. For programming projects, some Java support code (e.g., data parsers, solver adapters) can be provided, but students are not enforced to uptake it.
Required: CSCI-0320 or CSCI-0330, or consent of the instructor. Recommended: CSCI-0530 (or MATH-0520, MATH-0540), CSCI-1570
Limited to 20 students.