Lecture  MWF: 9:00 AM  9:50 AM (ENGR 616) 
Lecturer  Joseph Picone, Professor Office: EA 703A Office Hours: (MWF) 8 AM  9 AM, 10 AM  11 AM Phone: 2152044841 Email: picone@temple.edu Skype: joseph.picone 
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Website  http://www.isip.piconepress.com/courses/temple/ece_3522 
Textbook  Oliver Ibe
Fundamentals of Applied Probability and Random Processes, 2nd Edition Academic Press July 7, 2014, 456 pages ISBN: 9780128008522 URL: Fundamentals of Applied Probability and Random Processes (2nd Edition) 
Reference Textbooks  R.E. Walpole
Probability and Statistics for Engineers and Scientists, 9th Edition Pearson January 6, 2011, 816 pages ISBN: 9780321629111 URL: Probability and Statistics for Engineers and Scientists (9th Edition) R.A. Bailey Design of Comparative Experiments Cambridge Series in Statistical and Probabilistic Mathematics (Book 25) April 17, 2008, 346 pages URL: Design of Comparative Experiments 
Other Reference Materials 
MATLAB: The Statistical Toolbox
The R Project for Statistical Computing Online Statistics Education: An Interactive Multimedia Course of Study Introduction to Probability, Statistics and Random Processes 
Prerequisites  C or better in ECE 3512 


Exam No. 1  10% 
Exam No. 2  10% 
Exam No. 3  10% 
Final Exam  10% 
Quizzes  20% 
Computer Assignments  30% 
Homework Assignments  10% 
TOTAL:  100% 






Sects. 1.1  1.6 
Basic Probability Concepts: Sample Space and Events Elementary Set Theory Probability Definitions and Properties 


Sects. 1.7  1.9 
Basic Probability Concepts: Conditional Probabilities Total Probability Bayes Theorem The Binary Symmetric Channel Tree Diagrams Independent Events 


Sects. 1.10  1.13 
Basic Probability Concepts: Combined Experiments Combinatorial Analysis Reliability Theory Quiz 






Sects. 2.1  2.5 
Random Variables: Definition of a Random Variable Distribution Functions Discrete Random Variables 


Sects. 2.6  2.7 
Random Variables: Continuous Random Variables Examples Quiz 


Sects. 3.1  3.4 
Moments and Expectations: Averages and Expectations FirstOrder Moments General Moments Variance and Standard Deviation 


Sects. 3.5  3.8 
Conditional Expectations: Conditional Events The Markov Inequality The Chebyshev Inequality Examples 


Sects. 4.1, 4.10, 4.11 
Special Probability Functions: The Uniform Distribution The Normal (Gaussian) Distribution Quiz 


Sects. 4.2  4.9, 4.12  4.14 
Special Probability Functions: Bernoulli Trials Binomial Distribution Exponential Distribution 


Sects. 5.1  5.5 
Multiple Random Variables: Joint CDFs Discrete Bivariate Random Variables Computations and Examples 


Sects. 5.6  5.10 
Multiple Random Variables: Conditional Distributions and Probabilities Covariance Multivariate Random Variables Quiz 


6.1  6.3 
Functions of Random Variables: Functions of Random Variables Expectations of Random Variables Expectation of a Conditional Expectation 


Exam No. 1 
Chapters 1  4 


Sects. 6.4  6.7 
Functions of Random Variables: Sums of Independent Random Variables Minimum of Two Independent Random Variables Maximum of Two Independent Random Variables 


Sects. 6.8  6.11 
Functions of Random Variables: Two Functions of Two Random Variables A Sum of Two Correlated Random Variables Weak Law of Large Numbers Strong Law of Large Numbers The Central Limit Theorem Order Statistics 


Sects. 7.1  7.3 
Transform Methods: Characteristic Functions Moment Generating Property Sums of Independent Random Variables The Laplace Transform 


Sects. 7.4, 7.5 
Transform Methods: The zTransform Moment Generating Property Sums of Independent Random Variables Examples Quiz 


Sects. 8.1  8.5 
Descriptive Statistics: Measures of Central Tendency Measures of Dispersion Visualizations 


Sects. 8.6, 8.7 
Descriptive Statistics: Skewness Peakedness Examples More Visualizations 


Sects. 9.1, 9.2 
Inferential Statistics: Sampling Theory The Sample Mean Variance of the Sample Mean The Sample Variance Sampling Distributions Quiz 














Sects. 9.3 
Inferential Statistics: Estimation Theory Unbiased Estimaters Efficient Estimators Consistent Estimators Confidence Intervals Maximum Likelihood Estimation 


Sects. 9.4 
Inferential Statistics: Maximum Likelihood Estimation Minimum Mean Squared Error Estimation Hypothesis Testing Procedure Type I and II Errors 


Sect. 9.5 
Inferential Statistics: OneTailed and TwoTailed Tests Example Quiz 


Sect. 9.5 
Inferential Statistics: Regression Analysis Multivariate Linear Regression Review 


Sects. 10.1  10.4 
Random Processes: Characterization Mean and Autocorrelation Autocovariance Crosscorrelation and Crosscovariance 


Exam No. 2 
Chapters 5  8 


Sects. 10.6 
Random Processes: Stationarity Autocorrelation Properties Covariance Properties 


Sect. 10.7 
Random Processes: Ergodicity Power Spectral Density White Noise 


Sect. 10.8 
Random Processes: DiscreteTime Random Processes Autocorrelation Covariance Power Spectral Density 


Secs. 11.1  11.4 
Linear Systems: Deterministic Inputs ContinuousTime Inputs DiscreteTime Inputs Power Spectral Density (Revisited) 


Sects. 11.5, 11.6 
Linear Systems: Moving Average Processes Autoregressive Moving Average Processes Filters Frequency Domain Analysis 


Sects. 12.1  12.5 
Special Random Processes: Bernoulli Processes Random Walk Gaussian Process Poisson Processes 


Notes 
Principal Components Analysis: Covariance Eigenvalues and Eigenvectors Whitening Transformations 


Notes 
Maximum Likelihood Classification: The Binary Symmetric Channel The TwoClass Problem Maximum Likelihood Threshold Decoding 


Notes 
Maximum Likelihood Classification: 2D Classification Examples Visualization of Variance Overlappling Gaussians Other Special Distributions 


12.6, 12.7 
Hidden Markov Models: x Markov Processes FirstOrder Markov Processes Observable Models Examples of Hidden Models 


12.8  12.10  Hidden Markov Models: Definitions Basic Calculations Three Related Challenges Parameter Estimation Continuous Distributions Gaussian Mixture Models 


Exam No. 3 
Chapters 9  12.5 


Special Topics  Clustering: Hierarchical Approaches Agglomerative Clustering KMeans Clustering Examples 


Special Topics  Clustering: Quality Control Example Parameter Estimation Maximum Likelihood Classification 


Special Topics  Clustering: Gaussian Mixture Models The Expectation Maximization Theorem Examples 


Special Topics  Fun With Statistics: Game Shows Coin Tosses Signal Processing 


Final Exam (08:00  10:00 AM) 
Special Topics (Comprehensive) 





1.2, 1.14, 1.16, 1.20, 1.21, 1.27, 1.34, 1.38, 1.46, 1.49 


2.2, 2.4, 2.9, 2.15, 2.19, 2.21, 2.25, 2.34 


3.1, 3.5, 3.8, 3.10, 3.18, 3.20, 3.24, 3.25, 3.26, 3.27 


4.3, 4.6, 4.8, 4.19, 4.44 4.48, 4.58, 4.59, 4.61, 4.63 


5.1, 5.4, 5.9, 5.14, 5.16, 5.17, 5.18, 5.20, 5.22, 5.23 


6.1, 6.2, 6.8, 6.9, 6.11, 6.17, 6.22, 6.26, 6.32, 6.35 


7.1, 7.7, 7.9, 7.12, 7.17, 7.22 


8.3, 8.6, 8.13 


9.1, 9.7, 9.10, 9.13, 9.14, 9.18 


10.2, 10.6, 10.12, 10.16, 10.18, 10.25, 10.29, 10.35, 10.45, 10.46 


11.6, 11.8, 11.14, 11.17, 11.20, 11.23, 11.26, 11.30 


12.7, 12.10, 12.12, 12.46, 12.51 





Simple Statistics 


Regression and Histograms 


Variance 


Model Fitting 


Covariance and Correlation 


Means and Variances Revisited 


Visualization 


Central Limit Theorem 


Signal to Noise Ratios and Filtering 


Statistical Significance 


Autocorrelation and Power Spectral Density 


Principal Components Analysis 


Maximum Likelihood Classification 


TBD 