2,014
The number of undergraduate students, 2014-15 school year.
This course is concerned with modeling of decisions and analysis of models to develop a systematic approach to making decisions. This course introduces probability theory as the fundamental mathematical basis for the development of techniques for solving typical problems faced in making engineering decisions in industry and government. The aim of this course is to teach students to think structurally about decision-making problems. Extensive use of case studies gets students involved in real world situations.
Decision topics include research allocation, logistics, scheduling, sequential decision making, siting of facilities, investment decisions and other problems for decision making under uncertainty.
Topics:
Decision topics include research allocation, logistics, scheduling, sequential decision making, siting of facilities, investment decisions and other problems for decision making under uncertainty.
This course is an elective for both electrical and computer engineering majors. The goals are to provide the students with systematic approaches to making decisions, and to provide case studies for illustrations of these concepts.
A. After the first four weeks of class, the students should be able to do the following:
1. Perform fundamental resource allocation analysis using linear programming (a,c,e,k)
2. Make scheduling and assignment decisions using network flow concepts (a,c,e)
3. Model the decision process in a programming framework (a,j,k)
B. After the first eight weeks of class, the students should be able to do all of the items listed under A above, plus the following:
1. Perform sequential decision making in a dynamic programming environment (a,c,e)
2. Use deterministic programming techniques to solve decision making problems (a,c,e,k,j,m
3. Perform fundamental probability analysis (a, c, e, l)
C. After the first 12 weeks of class, the students should be able to do all of the items listed under A and B above, plus the following:
1. Perform basic statistical analysis including estimation (a, c, e, l)
2. Apply probabilistic concepts to the modeling of uncertainty in decision-making (a, c, e, l)
3. Apply conditional probability to engineering decision-making problems (a,c,e,l,k,m)
D. After the full 15 weeks of class and laboratory experiment #4, the students should be able to do all of the items listed under A, B, and C above, plus the following:
1. Perform decision making under certainty and uncertainty for case studies (a, c, e, k)
2. Present results of a decision making process (b, d, f, g)
3. Prepare a presentation of case studies and demonstrate the skills of convincing others of the soundness of recommended actions (a,b,c,d,e,f,g,h,i,j,h,l,m)
DEPARTMENT OF ELECTRICAL
AND COMPUTER ENGINEERING
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