EE 559 Machine Learning I Supervised Methods Spring 2025 Units 4 Instructor Mohammad Reza Rajati PhD Office Location PHE 412 Office Hours Right after the lecture by appointment rajati usc edu Include EE 559 in subject TA s TBD Office Hours TBA Office Location TBA Course Producer s TBD usc edu Include EE 559 in subject tbd usc edu Include EE 559 in subject Lecture s Tuesday Thursday 12 00 noon 1 50 pm OHE 122 Discussion s Friday 11 00 11 50 am OHE 122 Webpages Piazza Class Page for everything except grades and USC Brightspace Class Page for grades and GitHub for code submission All HWs handouts solutions will be posted in PDF format Student has the responsibility to stay current with webpage material Prerequisites No formal pre requisites Prior courses in multivariable calculus linear algebra and probability This course is a prerequisite to EE 660 Corequisites EE 503 EE 510 Other Requirements Basic computer skills e g plotting Python Matlab R etc Note Students need to be familiar with Python programming or be willing to learn Python Tentative Grading Assignments 45 Letter Grade Distribution Midterm Exam 25 Final Exam 30 Participation on Piazza 5 93 00 A 73 00 76 99 C 90 00 92 99 A 70 00 72 99 C 87 00 89 99 B 67 00 69 99 D 83 00 86 99 B 80 00 82 99 B 77 00 79 99 C 59 99 63 00 66 99 D 60 00 62 99 D F 2 EE 559 Syllabus January 14 2025 Disclaimer Although the instructor does not expect this syllabus to drastically change he reserves every right to change this syllabus any time in the semester If you have a question about the material or logistics of the class Note on e mail vs Piazza and wish to ask it electronically please post it on the piazza page not e mail Often times if one student has a question comment other also have a similar question comment Use private Piazza posts with the professor TA graders only for issues that are specific to your individually e g a scheduling issue or grade issue Minimize the use of email to the course staff and only use it when absolutely necessary Catalogue Description Distribution free and probabilistic methods for supervised classifica tion and regression learning algorithms optimization techniques feature space transformations parametric and nonparametric methods Bayes decision theory artificial neural networks Course Objectives Upon successful completion of this course a student will Broadly understand major algorithms used in supervised machine learning Understand the difference between supervised and unsupervised learning techniques Understand regression techniques Understand resampling methods including cross validation and bootstrap Understand methods of evaluation of classifiers and regression models Understand statistical and distribution free pattern recognition techniques Understand density estimation techniques Understand kernel methods for regression and classification Understand dimensionality reduction feature creation and regularization Understand unsupervised learning methods that serve as pre processing for supervised meth Understand feedforward neural networks and deep learning ods Exam Dates Midterm Exam in person Thursday March 13 12 00 Noon 1 50 PM Final Exam Wednesday May 14 2 00 4 00 PM as set by the university Important Note Please make absolutely sure that you can make the above dates No make up exams can be offered for any reason whatsoever Moreover no online exam will be offered to on campus students for any reason If a student misses Midterm 1 due to a valid reason e g documented medical or family emergency the grade of Midterm 2 will be considered as the grade of Midterm 1 If a student misses Midterm 2 due to a valid reason they will receive a grade of IN Incomplete and they must take the exam in the next semester with the students of that semester Unexcused absence in an exam warrants a grade of zero EE 559 Syllabus January 14 2025 3 Textbooks Required Textbooks 1 The Elements of Statistical Learning 2nd Edition Authors Trevor Hastie Robert Tibshirani and Jerome Friedman Springer 2009 ISBN 13 978 0 387 84857 0 2 Gareth James Daniela Witten Trevor Hastie and Robert Tibshirani An Introduction to Statistical Learning with Applications in R Springer 2021 ISLR Available at https web stanford edu hastie ISLRv2 website pdf Recommended Textbooks 1 Gareth James Daniela Witten Trevor Hastie and Robert Tibshirani An Introduction to Statistical Learning with Applications in Python Springer 2023 2 Pattern Classification 2nd Edition Authors Richard O Duda Peter E Hart and David G Stork Wiley 2001 ISBN 13 978 81 265 1116 7 3 Applied Predictive Modeling 1st Edition Authors Max Kuhn and Kjell Johnson Springer 2016 ISBN 13 978 1 4614 6848 6 4 Machine Learning An Algorithmic Perspective 2nd Edition Author Stephen Marsland CRC Press 2014 ISBN 13 978 1 4614 7137 0 5 Pattern Recognition and Machine Learning 1st Edition Author Christopher Bishop Springer 2006 ISBN 13 978 0 387 31073 2 6 Pattern Recognition 1st Edition Author Sergio Theodoridis Academic Press 2009 ISBN 13 978 1 597492720 7 Computer Age Statistical Inference Algorithms Evidence and Data Science 1st Edition Authors Bradley Efron and Trevor Hastie Cambridge University Press 2016 ISBN 13 978 1107149892 8 Deep Learning 1st Edition Authors Ian Goodfellow and Yoshua Bengio Springer 2009 ISBN 13 978 0 262 03561 3 9 Neural Networks and Learning Machines 3rd Edition Author Simon Haykin Pearson 2008 ISBN 13 978 0131471399 Grading Policies The letter grade distribution table guarantees the minimum grade each student will receive based on their final score When appropriate relative performance measures will be used to assign the final grade at the discretion of the instructor Final grades are non negotiable and are assigned at the discretion of the instructor If you cannot accept this condition you should not enroll in this course 4 EE 559 Syllabus January 14 2025 Your lowest homework grade and half of your second lowest homework grade will be dropped from the final grade For example if you received 90 85 10 95 65 80 100 your homework score will be 0 5 65 80 85 90 95 100 87 72 instead of 10 65 80 85 90 95 100 75 This policy makes up for missing assignments because of heavy workload sickness etc Remember that if you miss an assignment because of heavy workload in other courses and then miss another one because of sickness only the second assignment s grade will be completely dropped from your score Be aware of this when you decide not to submit an assignment because later you may