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: 215-204-4841 Email: picone@temple.edu Skype: joseph.picone |
Social Media |
https://www.facebook.com/groups/temple.engineering.ece3522/
temple.engineering.ece3522@groups.facebook.com |
http://groups.google.com/group/temple-engineering-ece3522
temple-engineering-ece3522@googlegroups.com |
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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: 978-0128008522 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: 978-0321629111 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 |
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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% |
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Sects. 1.1 - 1.6 |
Basic Probability Concepts: Sample Space and Events Elementary Set Theory Probability Definitions and Properties |
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Sects. 1.7 - 1.9 |
Basic Probability Concepts: Conditional Probabilities Total Probability Bayes Theorem The Binary Symmetric Channel Tree Diagrams Independent Events |
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Sects. 1.10 - 1.13 |
Basic Probability Concepts: Combined Experiments Combinatorial Analysis Reliability Theory Quiz |
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Sects. 2.1 - 2.5 |
Random Variables: Definition of a Random Variable Distribution Functions Discrete Random Variables |
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Sects. 2.6 - 2.7 |
Random Variables: Continuous Random Variables Examples Quiz |
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Sects. 3.1 - 3.4 |
Moments and Expectations: Averages and Expectations First-Order Moments General Moments Variance and Standard Deviation |
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Sects. 3.5 - 3.8 |
Conditional Expectations: Conditional Events The Markov Inequality The Chebyshev Inequality Examples |
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Sects. 4.1, 4.10, 4.11 |
Special Probability Functions: The Uniform Distribution The Normal (Gaussian) Distribution Quiz |
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Sects. 4.2 - 4.9, 4.12 - 4.14 |
Special Probability Functions: Bernoulli Trials Binomial Distribution Exponential Distribution |
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Sects. 5.1 - 5.5 |
Multiple Random Variables: Joint CDFs Discrete Bivariate Random Variables Computations and Examples |
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Sects. 5.6 - 5.10 |
Multiple Random Variables: Conditional Distributions and Probabilities Covariance Multivariate Random Variables Quiz |
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6.1 - 6.3 |
Functions of Random Variables: Functions of Random Variables Expectations of Random Variables Expectation of a Conditional Expectation |
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Exam No. 1 |
Chapters 1 - 4 |
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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 |
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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 |
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Sects. 7.1 - 7.3 |
Transform Methods: Characteristic Functions Moment Generating Property Sums of Independent Random Variables The Laplace Transform |
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Sects. 7.4, 7.5 |
Transform Methods: The z-Transform Moment Generating Property Sums of Independent Random Variables Examples Quiz |
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Sects. 8.1 - 8.5 |
Descriptive Statistics: Measures of Central Tendency Measures of Dispersion Visualizations |
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Sects. 8.6, 8.7 |
Descriptive Statistics: Skewness Peakedness Examples More Visualizations |
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Sects. 9.1, 9.2 |
Inferential Statistics: Sampling Theory The Sample Mean Variance of the Sample Mean The Sample Variance Sampling Distributions Quiz |
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Sects. 9.3 |
Inferential Statistics: Estimation Theory Unbiased Estimaters Efficient Estimators Consistent Estimators Confidence Intervals Maximum Likelihood Estimation |
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Sects. 9.4 |
Inferential Statistics: Maximum Likelihood Estimation Minimum Mean Squared Error Estimation Hypothesis Testing Procedure Type I and II Errors |
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Sect. 9.5 |
Inferential Statistics: One-Tailed and Two-Tailed Tests Example Quiz |
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Sect. 9.5 |
Inferential Statistics: Regression Analysis Multivariate Linear Regression Review |
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Sects. 10.1 - 10.4 |
Random Processes: Characterization Mean and Autocorrelation Autocovariance Crosscorrelation and Crosscovariance |
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Exam No. 2 |
Chapters 5 - 8 |
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Sects. 10.6 |
Random Processes: Stationarity Autocorrelation Properties Covariance Properties |
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Sect. 10.7 |
Random Processes: Ergodicity Power Spectral Density White Noise |
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Sect. 10.8 |
Random Processes: Discrete-Time Random Processes Autocorrelation Covariance Power Spectral Density |
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Secs. 11.1 - 11.4 |
Linear Systems: Deterministic Inputs Continuous-Time Inputs Discrete-Time Inputs Power Spectral Density (Revisited) |
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Sects. 11.5, 11.6 |
Linear Systems: Moving Average Processes Autoregressive Moving Average Processes Filters Frequency Domain Analysis |
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Sects. 12.1 - 12.5 |
Special Random Processes: Bernoulli Processes Random Walk Gaussian Process Poisson Processes |
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Notes |
Principal Components Analysis: Covariance Eigenvalues and Eigenvectors Whitening Transformations |
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Notes |
Maximum Likelihood Classification: The Binary Symmetric Channel The Two-Class Problem Maximum Likelihood Threshold Decoding |
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Notes |
Maximum Likelihood Classification: 2D Classification Examples Visualization of Variance Overlappling Gaussians Other Special Distributions |
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12.6, 12.7 |
Hidden Markov Models: x Markov Processes First-Order Markov Processes Observable Models Examples of Hidden Models |
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12.8 - 12.10 | Hidden Markov Models: Definitions Basic Calculations Three Related Challenges Parameter Estimation Continuous Distributions Gaussian Mixture Models |
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Exam No. 3 |
Chapters 9 - 12.5 |
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Special Topics | Clustering: Hierarchical Approaches Agglomerative Clustering K-Means Clustering Examples |
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Special Topics | Clustering: Quality Control Example Parameter Estimation Maximum Likelihood Classification |
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Special Topics | Clustering: Gaussian Mixture Models The Expectation Maximization Theorem Examples |
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Special Topics | Fun With Statistics: Game Shows Coin Tosses Signal Processing |
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Final Exam (08:00 - 10:00 AM) |
Special Topics (Comprehensive) |
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1.2, 1.14, 1.16, 1.20, 1.21, 1.27, 1.34, 1.38, 1.46, 1.49 |
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2.2, 2.4, 2.9, 2.15, 2.19, 2.21, 2.25, 2.34 |
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3.1, 3.5, 3.8, 3.10, 3.18, 3.20, 3.24, 3.25, 3.26, 3.27 |
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4.3, 4.6, 4.8, 4.19, 4.44 4.48, 4.58, 4.59, 4.61, 4.63 |
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5.1, 5.4, 5.9, 5.14, 5.16, 5.17, 5.18, 5.20, 5.22, 5.23 |
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6.1, 6.2, 6.8, 6.9, 6.11, 6.17, 6.22, 6.26, 6.32, 6.35 |
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7.1, 7.7, 7.9, 7.12, 7.17, 7.22 |
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8.3, 8.6, 8.13 |
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9.1, 9.7, 9.10, 9.13, 9.14, 9.18 |
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10.2, 10.6, 10.12, 10.16, 10.18, 10.25, 10.29, 10.35, 10.45, 10.46 |
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11.6, 11.8, 11.14, 11.17, 11.20, 11.23, 11.26, 11.30 |
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12.7, 12.10, 12.12, 12.46, 12.51 |
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Simple Statistics |
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Regression and Histograms |
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Variance |
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Model Fitting |
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Covariance and Correlation |
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Means and Variances Revisited |
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Visualization |
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Central Limit Theorem |
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Signal to Noise Ratios and Filtering |
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Statistical Significance |
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Autocorrelation and Power Spectral Density |
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Principal Components Analysis |
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Maximum Likelihood Classification |
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TBD |