Lecture  MWF: 10:00  10:50 AM (ENGR 308 / Online) 
Lecturer 
Joseph Picone, Professor Office: ENGR 718 Office Hours: (MWF) 08:00 AM  10:00 AM Phone: 2152044841 (desk); 7088482846 (cell  preferred) Email: picone@temple.edu Zoom: picone@temple.edu or joseph.picone@gmail.com 
Google Group URL:
https://groups.google.com/g/temple_engineering_ece8527
Google Group Email: temple_engineering_ece8527@googlegroups.com 

Website  http://www.isip.piconepress.com/courses/temple/ece_8527 
Required Textbook 
A. Lindholm, N. Wahlstrom, F. Lindsten and T. Schon, Machine Learning: A First Course for Engineering and Scientists, Cambridge University Press, New York, New York, USA, ISBN: 9781108843607, pp. 338, 2022. URL: http://smlbook.org/book/smlbookdraftlatest.pdf 
Reference Textbooks 
There are a lot of textbooks available online. Most modern textbooks focus on neural networks or deep learning. While these are important topics, our goal in this course is to give you a broad perspective of the field. Technology changes quickly, so you need to have a good background in the fundamentals, and need to understand how to do more than just "button push."
Those wishing to build their theoretical background in
this area will find this book useful:

Prerequisites  ECE 8527: ENGR 5022 (minimum grade: B) ENGR 5033 (minimum grade: B) ECE 4527: ECE 3512 (minimum grade: C) ECE 3522 (minimum grade: C) 
Pattern recognition theory and practice is concerned with the
design, analysis, and development of methods for the
classification or description of patterns, objects, signals, and
processes. At the heart of this discipline is our ability infer
the statistical behavior of data from limited data sets, and to
assign data to classes based on generalized notions of distances
in a probabilistic space. Many commercial applications of pattern
recognition exist today, including voice recognition (e.g., Amazon
Alexa), fingerprint classification (e.g., MacBook Pro touch bar),
and retinal scanners (e.g., your favorite cheesy scifi movie).
Machine learning is a field that is at least 50 years old. Recent
advances in deep learning, starting around 2005, have
have revolutionized the field. Today, machine learning is one
of the most active areas of engineering and is enjoying
unprecedented levels of success. However, to understand why
these techniques work, we must build a background in traditional
pattern recognition and machine learning concepts such as
maximum likelihood decision theory, Bayesian estimation,
nonparametric methods such as decision trees and support
vector machines and temporal modeling approaches such as
hidden Markov models. This course is designed to give you
a strong background in fundamentals, yet also introduce you
to the tools necessary to implement these algorithms.


Exam No. 1  15% 
Exam No. 2  15% 
Exam No. 3  15% 
Final Exam  15% 
Computer Homework  40% 
TOTAL:  100% 







Introduction: An Overview of Machine Learning  1.1  1.3 



Decision Theory: Bayes Rule  9.1, 9.2 



Decision Theory: Gaussian Classifiers  9.3, 9.4 



Decision Theory: Generalized Gaussian Classifiers  9.5  9.A 



Parameter Estimation: Maximum Likelihood  3.1 



Parameter Estimation: The Bayesian Approach  9.2 



Decision Theory: Discriminant Analysis  10.4 



Parameter Estimation: The Expectation Maximization Theorem  10.1 



Hidden Markov Models: Introduction  Notes 



Hidden Markov Models: Evaluation  Notes 



Hidden Markov Models: Decoding and Dynamic Programming  Notes 



Hidden Markov Models: Parameter Reestimation and Continuous Distributions  Notes 



Exam No. 1 (Lectures 01  09)  Notes 



Parameter Estimation: Information Theory Review  Notes 



Parameter Estimation: Discriminative Training  Notes 



Experimental Design: Foundations of Machine Learning  11.1  11.6 



Experimental Design: Statistical Significance and Confidence  Notes 



Parameter Estimation: Jacknifing, Boostrapping and Combining Classifiers  4.4 – 4.6, 7.1  7.5 



Parameter Estimation: Nonparametric Techniques  4.5, 4.6 



Unsupervised Learning: Clustering  10.2 



Unsupervised Learning: Hierarchical Clustering  10.3  10.5 



Supervised Learning: Decision Trees  2.3, 7.1  7.5 



Supervised Learning: Support Vector Machines  8.1  8.B 



Neural Networks: Introduction  6.1, 6.2, 6.A 



Neural Networks: Vanishing Gradients and Regularization  3.3, 5.3 



Exams: Exam No. 2 (Lectures 1024)  Notes 



Neural Networks: Linear and Logistic Regression  3.1  3.A 



Neural Networks: Deep Learning  Notes 



Neural Networks: Alternative Architectures  11.1  11.3 



Neural Networks: Deep Generative Models and Autoencoders  10.3 



Neural Networks: Alternative Supervision Strategies  6.1, 6.2 



Neural Networks: Transfer Learning  Notes 



Neural Networks: Alternative Activation Functions and Optimizers  5.4  5.6 



Attention and Transformers  Notes 



More About Transformers  10.3  10.4 



Transformer Architectures  Notes 



Expainability in AI  Notes 



Trustworthiness in AI  Notes 



Exam No. 3 (Lectures 25  37)  Notes 



Applications: Human Language Technology (Sequential Decoding)  Notes 



Applications: Large Language Models and the ChatGPT Revolution  Notes 



Applications: Sampling Techniques and Quantum Computing  Notes 



Final Exam (08:00 AM  10:00 AM)  N/A 






Gaussian Distributions 


Bayesian Decision Theory 


ML and Bayesian Parameter Estimation 


Gaussian Mixture Distribution Parameter Estimation 


Dynamic Programming 


Hidden Markov Models (HHMs) 


Information Theory and Statistical Significance 


LDA, KNearest Neighbors and KMeans Clustering 


Bootstrapping, Bagging and Combining Classifiers 


Nonparametric Classifiers, ROC Curves and AUC 


Multilayer Perceptrons 


Convolutional Neural Networks 


Transformers 