ENGG2760 Probability for Engineers


Course code ENGG2760
Course title Probability for Engineers
Course description A first course in the fundamentals of probability theory and their applications in engineering. Topics include sample space and events, counting, axioms of probability, conditional probability, independence of events, discrete and continuous distributions, random variables, joint distributions, and limit theorems.
Unit(s) 2
Course level Undergraduate
Exclusion ENGG2430 or 2450 or 2470 or ESTR2002 or 2005 or 2012 or 2018 or 2308 or 2362 or IERG2470 or MIEG2440
Semester 1
Grading basis Graded
Grade Descriptors A/A-:  EXCELLENT – exceptionally good performance and far exceeding expectation in all or most of the course learning outcomes; demonstration of superior understanding of the subject matter, the ability to analyze problems and apply extensive knowledge, and skillful use of concepts and materials to derive proper solutions.
B+/B/B-:  GOOD – good performance in all course learning outcomes and exceeding expectation in some of them; demonstration of good understanding of the subject matter and the ability to use proper concepts and materials to solve most of the problems encountered.
C+/C/C-: FAIR – adequate performance and meeting expectation in all course learning outcomes; demonstration of adequate understanding of the subject matter and the ability to solve simple problems.
D+/D: MARGINAL – performance barely meets the expectation in the essential course learning outcomes; demonstration of partial understanding of the subject matter and the ability to solve simple problems.
F: FAILURE – performance does not meet the expectation in the essential course learning outcomes; demonstration of serious deficiencies and the need to retake the course.
Learning outcomes At the conclusion of the course, students should be able to
1. define and understand the fundamental concepts in probability
2. identify, formulate, and solve simple engineering problems involving randomness
(for reference only)
Essay test or exam:65%
Homework or assignment:25%
Recommended Reading List 1. Dimitri P. Bertsekas and John N. Tsitsiklis, Introduction to Probability, Athena Scientific, 2nd Edition, 2008
2. Sheldon M. Ross, A First Course in Probability, Pearson, 9th Edition, 2014
3. Richard A. Johnson, Irwin Miller, and John E. Freund, Miller and Freund’s Probability and Statistics for Engineers, Pearson, 9th Edition, 2017


CSCIN programme learning outcomes Course mapping
Upon completion of their studies, students will be able to:  
1. identify, formulate, and solve computer science problems (K/S);
2. design, implement, test, and evaluate a computer system, component, or algorithm to meet desired needs (K/S);
3. receive the broad education necessary to understand the impact of computer science solutions in a global and societal context (K/V);
4. communicate effectively (S/V);
5. succeed in research or industry related to computer science (K/S/V);
6. have solid knowledge in computer science and engineering, including programming and languages, algorithms, theory, databases, etc. (K/S);
7. integrate well into and contribute to the local society and the global community related to computer science (K/S/V);
8. practise high standard of professional ethics (V);
9. draw on and integrate knowledge from many related areas (K/S/V);
Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes; T = Teach; P = Practice; M = Measured