ELEC2844: Probabilistic Systems Analysis

The University of Hong Kong, Second Semester, 2023–24

Important Information

You should be mindful of the University's policy on plagiarism.
Departmental Guideline on Student Feedback on Teaching and Learning (SFTL):
- Reserve 15-minute of your lecture time within the period of 5 April 2024 to 3 May 2024 to administer the online course survey in class
- Inform your students in advance about the date of doing online SFTL questionnaire so that they could bring their mobile/internet device (such as laptop, tablet or smartphone) to complete the evaluation at the scheduled time — we will do that on April 9
- Highlight to students that their valuable feedback and comments are of great important for continuous improvement of the courses

Calendar entry of ELEC2844 (subject to change as needed):

This course aims to introduce students to the modelling and analysis of real world phenomena with the tools of probability and statistics. It involves both theoretical and computational components, where probabilistic concepts are taught through many engineering examples ranging from pattern analysis and image processing to forecasting and finance. Topics include random variables, independence and conditional probability, mathematical expectation, functions of random variables, moment generating functions and characteristics functions, transforms, simple random processes, fundamental sampling distributions, estimation, hypothesis testing, and linear regression.

Pre-requisite: MATH1853 Linear algebra, probability & statistics
Mutually exclusive: STAT2601 Probability and statistics I

Assessment: 40% continuous assessment, 60% examination

Grading and Standards of Assessment

A: Exceptionally good performance demonstrating a superior understanding of the subject matter, a foundation of extensive knowledge, a skillful use of concepts and/or materials, and ability to analyze and evaluate problems.
B: Good performance demonstrating capacity to use the appropriate concepts, a good understanding of the subject matter, and an ability to handle the problems and materials encountered in the course.
C: Adequate performance demonstrating an adequate understanding of the subject matter, an ability to handle relatively simple problems, and adequate preparation for moving on to more advanced work in the field.
D: Minimally acceptable performance demonstrating at least partial familiarity with the subject matter and some capacity to deal with relatively simple problems, but also demonstrating deficiencies serious enough to make it inadvisable to proceed further in the field without additional work.
F: Unacceptable performance demonstrating unfamiliarity with the subject matter, and lack of capacity to deal with relatively simple problems, and also demonstrating deficiencies serious enough to make it advisable to retake the course.

Course Learning Outcomes

Understand the use of probability in engineering systems analysis.
Understand the modeling of engineering problems and tackle through the application of probability and statistics.