Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download EE501:Stochastic Processes
Document related concepts
Transcript
University of Management & Technology School of Science & Technology Department of Electrical Engineering EE 306 Probability and Statistics for Engineers Lecture Schedule As per timetable on EED Website Pre-requisite None Saleem Ata Instructor(s) Office 5thFloor SEN Building Semester Spring2016 Credit Hours 3 Contact [email protected] Office Hours See office door or EED Website Course Description The course is a basic core course in Electrical Engineering which shall build necessary background for courses in the area of communication. Course will include in-depth knowledge of Basic Probability Theory, Discrete and Continuous Random Variables, Functions of Random Variables, Expectations, Joint Distributions, Moment generating functions. The course directly contributes to objectivesa, d, e and f of the HEC Electrical Engineering Curriculum. Expected Outcomes Upon completion of this course, students will: Students shall be able to use probability concepts and random variables as a tool to model and solve practical problems with applications in Engineering. The course strongly supports expected outcomes a, b, d, I and p of the HEC Electrical Engineering Curriculum. Required: Textbook(s) Grading Policy Intuitive Probability and Random Processes using Matlab, Steven M. Kay. Reference: A First course in Probability By Sheldon Ross Probability, Statistics, and Random Processes for Electrical Engineering By Alberto Leon Garcia Quizzes : 25% (All Announced) All quizzes will be announced. Quizzes will be 10-20 minutes. Quizzes could be open book or closed book. All are advised to bring their text books along. Midterm : 25% 60-70 minute exam. All topics covered before the midterm exam will be included. Final : 50% 120-150 minute exam. Will be comprehensive. Course Schedule Lecture 1 3 4 4 2 Topics Introduction to Probability Probabilistic Modeling Real-World Examples – Digital Communications Basic Probability Review of Set Theory Combinatorics Real World Example – Quality Control Basic Probability theory Conditional Probability Joint Events Statistically Independent Events Bayes’ Theorem Multiple Experiments Real World Example – Cluster Recognition Discrete Random Variables Probability of Discrete Random Variables Probability Mass Functions Transformation of Discrete Random Variables Cumulative Distribution Function Real World Example – Servicing Customers Expected Values for Discrete Random Variables Determining Averages from PMF Expected Values of Some Important Random Variables Expected Value for a Function of a Random Variable Variance and Moments of a Random Variable Characteristics Functions Estimating Means and Variances Real World Example – Data Compression Textbook (TB) / Reference (Ref) Readings TB Chap 1 Sheldon Ross Chap 1 TB Chap 3 Sheldon Ross Chap 2 TB Chap 4 TB Chap 5 TB Chap 6 Mid Term (8th Week) 2 Multiple Discrete Random Variables Jointly Distributed Random Variables Marginal PMF’s and CDF’s Independence of Multiple Random Variables Transformation of Multiple Random Variables Real World Example – Assembling Health Risks TB Chap 7 3 Conditional Probability Mass Functions Conditional Probability Mass Function Joint, Conditional and Marginal PMF’s Mean of the Conditional PMF Real World Example – Modeling Human Learning TB Chap 8 2 Continuous Random Variables Definition of a Continuous Random Variable PDF & Its Properties Important PDF’s Cumulative Distribution Function Transformations Mixed Random Variables Real World Example – Setting Clipping Levels TB Chap 10 3 Expected Values for Continuous Random Variables Determining the Expected Value Expected Values for Important PDF’s Expected Value for a Function of Random Variable Variance Moments Characteristic Functions Probability, Moments &Chebyshev Inequality Estimating the Mean and Variance Real World Example – Critical Software Testing TB Chap 11 2 Multiple Continuous Random Variables Jointly Distributed Random Variables Marginal PDF’s and Joint CDF Independence of Multiple Random Variables Transformations Expected Values Joint Moments Joint Characteristic Functions Real World Example – Optical Character Recognition TB Chap 12 2 Conditional Probability Density Functions Conditional PDF Joint, Conditional and Marginal PDF’s Mean of Conditional PDF Real World Example – Retirement Planning TB Chap 13 Final Term Exam (Comprehensive)
