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 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 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.
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.
General Information
Instructors:
Ugur Cetintemel - Serdar Kadioglu - {ugur, serdark}@cs.brown.edu
Teaching Assistants:
Tanay Padhi - Isaac Semaya - {tpadhi, isemaya}@cs.brown.edu
Class Room / Hours
CIT 477 (Lubrano) Tuesday 16:00 - 18:20
Office Hours:
Tuesday 1pm - 2pm (Serdar CIT 317)
| Name | Office Hours | |
|---|---|---|
Ugur Cetintemel | When? Where? | |
serdar_kadioglu@brown.edu | When? Where? |