EE 559 Machine Learning I Supervised Methods Units 4 Lecture MW 3 30 5 20 PM Pacific Time Discussion Thu 5 30 6 20 PM Pacific Time Location OHE 122 online and DEN Viterbi Course URL https courses uscden net d2l home Instructor B Keith Jenkins Office Hours TBA Contact Info Email jenkins sipi usc edu Please include EE 559 in the subject line Online piazza com Teaching Assistants TBA IT Help For help with coding machine learning algorithms consult piazza online forums or the TAs for help with other python coding working with datasets or using library routines in python also try online help and documentation and reference resources given below for help with USC supplied software or on campus networking consult USC ITS at https itservices usc edu contact EE 559 syllabus v1 Page 1 Course Description Catalogue Distribution free and probabilistic methods for supervised classification and regression learning algorithms optimization techniques feature space transformations parametric and nonparametric methods Bayes decision theory artificial neural networks Course Description Expanded Concepts and algorithms for pattern recognition and regression using machine learning are covered in depth The course will stress an understanding of different supervised learning algorithms at both theoretical and practical levels as well as their advantages and disadvantages Underlying fundamentals are emphasized including theory and origins of learning algorithms and criterion functions The goal is to give the student an understanding of some fundamental approaches to machine learning to enable further study and growth on their own The student s work will include mathematical analysis analytical understanding and writing and running code that learns from data A moderately sized project in the second half of the semester will involve developing and optimizing one or more machine learning systems to perform well on real world datasets This course is intended for graduate students in Electrical and Computer Engineering or related fields who wish to gain an understanding of and some experience with machine learning approaches tools and techniques Only supervised learning methods are covered in this course Learning Objectives After successfully completing this course the student will Have a perspective of different approaches to supervised machine learning for pattern classification and regression Understand the underlying math of a variety of supervised learning methods Be able to use statistical and non statistical techniques to solve machine learning problems Be able to code and run algorithms for learning from data Be able to optimize machine learning algorithms and systems and assess their overall performance Know how to create new techniques for machine learning where needed Prerequisite s Co Requisite s EE 503 and EE 510 Concurrent Enrollment Recommended Preparation knowledge of python e g at the level of EE 541 knowledge of multivariate calculus at the sophomore or junior undergraduate level For students that don t know python resources will be provided to help them learn python during the first few weeks of the semester leveraging from their knowledge of MATLAB students should allow some extra time for this if they don t know much python Course Notes The Desire2Learn D2L system will host the course website On the website will be posted video recordings of the lectures and discussion sessions lecture and discussion session notes additional instructor provided notes and handouts assignments and solutions Your graded assignments and scores will also be visible to you on the website Technological Proficiency and Hardware Software Required Python will be used throughout this class for homework assignments and the class project All students will be responsible for installing and maintaining their own python distribution EE 559 syllabus v1 Page 2 Required Readings and Supplementary Materials C M Bishop Pattern Recognition and Machine Learning Springer 2006 ISBN 13 978 0387 31073 2 Available from USC bookstore Amazon com and Springer com Required text Supplementary texts Kevin Murphy Probabilistic Machine Learning An Introduction MIT Press 2022 Preprint available for download at https probml github io pml book book1 html R O Duda P E Hart and D G Stork Pattern Classification Second Edition Wiley Interscience John Wiley and Sons Inc New York 2001 T Hastie R Tibshirani and J Friedman The Elements of Statistical Learning Data Mining Inference and Prediction Second Edition Springer 2009 Simon Haykin Neural Networks and Learning Machines 3rd Edition Pearson 2009 Ethem Alpaydin Introduction to Machine Learning Fourth Edition MIT Press 2020 I Goodfellow Y Bengio and A Clourville Deep Learning MIT Press 2016 Supplementary resources for Python Hans Fanghor Introduction to Python for Computational Science and Engineering 2016 PCSE available for free download at https github com fangohr introduction to python for computational science and engineering Fabio Nelli Python Data Analytics APress 2015 PDA available for download from USC Library http usc summon serialssolutions com search q Fabio 20Nelli 2C 20Python 20Data 20Anal ytics search ho t l en q Fabio 20Nelli 20Python 20Data 20Analytics The Python 3 Tutorial PT https docs python org 3 8 tutorial index html EU Python 3 Tutorial EUP Good for Chapters on object oriented programming class vs instance attributes and inheritance http www python course eu python3 course php Description and Assessment of Assignments 1 Homework assignments Homework assignments will on average consist of approximately 50 computer problems and 50 analytical problems Homework assignments will be posted on the course website when assigned and typically due 1 week later due date and time will be specified on the homework assignment Each homework assignment will be worth 10 points Starting with Homework 3 a portion of the homework score 1 2 points depending on the amount and complexity of coding that is needed for each homework assignment that includes coding will be based on quality of Python code that the student has written Guidelines for writing quality code will be given in discussion session as well as a handout on the course website Python 3 is required for all homework computer problems with the exception of the first 2 homework assignments for which you are also allowed to use MATLAB Some of the computer problems will require coding machine learning algorithms yourself without using