Description
The Probability and Random Process (PRP) is an undergraduate math course aimed at teaching the fundamentals of probability and random processes to model the uncertainty in human’s daily life and applied the knowledge gain into the field of engineering like signal processing and communications, Biomedical etc. This course is a core subject of ICT undergraduate programs at School of engineering and Applied Science. The course is designed to develop the student’s various soft skills like problem-solving, teamwork, critical thinking, and Monte Carlo simulation framework to model an uncertainty.
General Information
Course Objective
• Learn the basic concepts associated with probability theory
• To learn how to model uncertainty using probability
• Understand common probability distributions - Discrete and Continuous random variables, how to use it in applications
• Understand the concept of conditional expectation and its role in inference.
• Operations on single Random Variable and Pair of Random Variables
• Learn the Random Process and its applications
• To learn how to model uncertainty using probability
• Understand common probability distributions - Discrete and Continuous random variables, how to use it in applications
• Understand the concept of conditional expectation and its role in inference.
• Operations on single Random Variable and Pair of Random Variables
• Learn the Random Process and its applications
Learning Outcomes
At the end of this course, students shall be able to
• Learn PRP as a tool to understand core engineering application
• Associate probability theory to engineering practice
• Create a strong basis for Digital Communications, Information Theory, Coding Theory, Image Processing, Speech Analysis, Synthesis and Recognition, and similar courses that are commonly found in
• Learn a course to match the ABET standard
• Learn PRP as a tool to understand core engineering application
• Associate probability theory to engineering practice
• Create a strong basis for Digital Communications, Information Theory, Coding Theory, Image Processing, Speech Analysis, Synthesis and Recognition, and similar courses that are commonly found in
• Learn a course to match the ABET standard
Assessment / Evaluation
40% End-term examination
30% Mid-term examination
5% for Piazza class discussions
15% Assignments (Problem solving – Total five – Due at every 21 days )
10% Special Assignment-Report submission and Presentation: Modeling of daily Life Uncertainty (Modeling, Coding and Problem Solving)
Note: There will be no make-up evaluation component for mid-term examination, class presentation and project report submission. There will be one make-up examination for the students who miss end-term examination. For this the students must seek prior permission from the course coordinator. Student shall have to produce sufficient evidence to prove the genuineness of his/her absence from end-term examination. The schedule for end-term make-up exam will be announced later. The student who miss the make-up examination as well shall have to repeat the course.
30% Mid-term examination
5% for Piazza class discussions
15% Assignments (Problem solving – Total five – Due at every 21 days )
10% Special Assignment-Report submission and Presentation: Modeling of daily Life Uncertainty (Modeling, Coding and Problem Solving)
Note: There will be no make-up evaluation component for mid-term examination, class presentation and project report submission. There will be one make-up examination for the students who miss end-term examination. For this the students must seek prior permission from the course coordinator. Student shall have to produce sufficient evidence to prove the genuineness of his/her absence from end-term examination. The schedule for end-term make-up exam will be announced later. The student who miss the make-up examination as well shall have to repeat the course.
Attendance Policy:
Minimum 80% attendance is compulsory and grade drop will apply as per the AU attendance policy.
Special Assignment Details:
As part of the evaluation, students are expected to pick daily life uncertainty and apply whole subject knowledge to solve a unique problem and derive an inference based on modeling, coding and analysis. The details of which are given below:
• Each group consists of max two students. The students have autonomy to choose the partner.
• The objective of the special assignment is to encourage students to observe and find the daily life uncertainty and model it using concepts of probability and random processes.
• The Special Assignment will be carried out during the entire semester and the final report is to be submitted in the month of April 21, 2016.
Detailed instructions will be provided in class.
• Each group consists of max two students. The students have autonomy to choose the partner.
• The objective of the special assignment is to encourage students to observe and find the daily life uncertainty and model it using concepts of probability and random processes.
• The Special Assignment will be carried out during the entire semester and the final report is to be submitted in the month of April 21, 2016.
Detailed instructions will be provided in class.
Books
Text Book:
Scott Miller and Donald Childers. Probability and random processes, Academic Press, 2012.
References:
1. Bertsekas, Dimitri, and John Tsitsiklis. Introduction to Probability. 2nd Ed. Athena Scientific, 2008. ISBN: 9781886529236.
2. Grimmett, Geoffrey, and David Stirzaker. Probability and Random Processes. 3rd Ed. Oxford University Press, 2001.
3. Papoulis, A, and S. U. Pillai (2002), Probability, Random Variables and Stochastic Processes, 4th Edition, Tata McGraw-Hill.
4. A First Course in Probability, Sheldon Ross, 9th Edition, 2012, Pearson
5. Johnson, R. A., and Gupta, C. B. “Miller and Freund’s Probability and Statistics for Engineers.” Pearson Education.
Scott Miller and Donald Childers. Probability and random processes, Academic Press, 2012.
References:
1. Bertsekas, Dimitri, and John Tsitsiklis. Introduction to Probability. 2nd Ed. Athena Scientific, 2008. ISBN: 9781886529236.
2. Grimmett, Geoffrey, and David Stirzaker. Probability and Random Processes. 3rd Ed. Oxford University Press, 2001.
3. Papoulis, A, and S. U. Pillai (2002), Probability, Random Variables and Stochastic Processes, 4th Edition, Tata McGraw-Hill.
4. A First Course in Probability, Sheldon Ross, 9th Edition, 2012, Pearson
5. Johnson, R. A., and Gupta, C. B. “Miller and Freund’s Probability and Statistics for Engineers.” Pearson Education.
Announcements
Regarding PRP Answerbook of End-semester Exam and Assignments/Piazza participation Marks
Formula for Piazza class Participation (Total Weightage: 5 % - Out of 5 Marks)
Important : Class gathering on Friday (5/5/2017) at Lecture Room -106 on 11:10 A.M.
Special_Assignment_Presentation_Schedule.xlsx has been added to class homepage under Resources
| Name | Office Hours | |
|---|---|---|
Dhaval Patel | When? Where? | |
Parth Shah | When? Where? | |
Parthkumar Patel | When? Where? |
Homework
Homework
Due Date
Apr 18, 2017
Mar 24, 2017
Mar 3, 2017
Feb 23, 2017
Feb 3, 2017