\setcounter{numTAs}{0} \setcounter{totalSections}{2} \def\secNum{{"001","DL1",""}} \def\tenSchFileName{{"","",""}} \def\classTime{{"Tue from 06:15 pm to 08:45 pm in Drosdick Hall B62.","T from 06:15 pm to 08:45 pm in Online SYN.",""}} \def\classRm{{"Drosdick Hall B62.","Online",""}} \def\classLive{{"","",""}} \def\classInstructor{{"Xun Jiao","Xun Jiao",""}} \def\classInstrContact{{"https://www1.villanova.edu/university/engineering/academic-programs/departments/electrical-computer/directory.html","https://www1.villanova.edu/university/engineering/academic-programs/departments/electrical-computer/directory.html",""}} \def\classInstrOffHrs{{"Tue 04:00 pm to 06:00 pm","Tue 02:00 pm to 04:00 pm",""}} \def\classInstrLive{{"","",""}} \def\TA{{{""},{""},{""}}} \def\TAEmail{{{""},{""},{""}}} \def\TAOffHrs{{{""},{""},{""}}} \def\TARoom{{{""},{""},{""}}} \newcommand\semester{Fall 2025} \newcommand\rsemester{202620} \newcommand\courseNum{ECE 8487} \newcommand\courseName{Advanced Machine Learning} \newcommand\courseCoordinator{Xun Jiao} \newcommand\credits{3} \newcommand\contactHrs{3} \newcommand\lecture{1} \newcommand\lab{0} \newcommand\undergradCourse{0} \newcommand\isFreshmanCourse{0} \newcommand\isCustomElecPolicy{0} \newcommand\AIPolicyExists{0} \newcommand\isClassLive{0} \newcommand\isLabLive{0} \newcommand\meetingMiscExists{0} \newcommand\isClassInstrLive{0} \newcommand\isLabInstrLive{0} \newcommand\instrMiscExists{0} \newcommand\hasTARoom{0} \newcommand\meetingDesc{} \newcommand\meetingMisc{Special notes on meeting info go here, if specified} \newcommand\instructorMisc{Special notes on instructor(s), TA(s) go here, if specified} \newcommand\textBookExists{0} \newcommand\textBookReqd{0} \newcommand\textBookMiscExists{0} \newcommand\referencesExist{0} \newcommand\txtBkAuthExists{0} \newcommand\txtBkPublExists{0} \newcommand\txtBkYrExists{0} \newcommand\txtBkISBNExists{0} \newcommand\textBookTitle{} \newcommand\textBookAuth{} \newcommand\textBookPub{} \newcommand\textBookYr{} \newcommand\textBookISBN{} \newcommand\supplMaterials{Bishop: Pattern Recognition and Machine Learning} \newcommand\refPapers{References go here, if specified} \newcommand\textBookMisc{Special notes on textbook(s) go here, if specified} \newcommand\catalogDesc{Advanced Machine Learning covers three main areas: basic algorithmic foundations such as linear regression and neural networks, applications of machine learning in image classification and natural language processing, and hardware acceleration of machine learning using GPUs and customized silicon (e.g., TPU).} \newcommand\preReqs{None} \newcommand\coReqs{None} \newcommand\coreRequirement{Graduate Course} \newcommand\courseExpectation{Learn how to develop and implement machine learning models; learn the theoretical aspects of various machine learning algorithms.} \newcommand\ABETOutOneA{0} \newcommand\ABETOutOneB{0} \newcommand\ABETOutTwoA{0} \newcommand\ABETOutTwoB{0} \newcommand\ABETOutTwoC{0} \newcommand\ABETOutTwoD{0} \newcommand\ABETOutThree{0} \newcommand\ABETOutFourA{0} \newcommand\ABETOutFourB{0} \newcommand\ABETOutFourC{0} \newcommand\ABETOutFive{0} \newcommand\ABETOutSixA{0} \newcommand\ABETOutSixB{0} \newcommand\ABETOutSevenA{0} \newcommand\ABETOutSevenB{0} \newcommand\covTopics{\item Machine Learning Overview; \item Supervised Learning; \item Linear Models; \item Tree Models; \item Neural Networks; \item PyTorch; \item Applications; \item Hardware Acceleration of ML; \item Research Papers. } \newcommand\isScheduleExternal{0} \newcommand\isScheduleCommon{1} \newcommand\scheduleRows{17} \newcommand\scheduleCols{2} \newcommand\scheduleHeight{1} \newcommand\schedule{\begin{table}[h!] \centering \caption*{Tentative Schedule for \textbf{All Sections}} \vspace{0.05in} {\renewcommand{\arraystretch}{1.5} \small \begin{tabularx}{\linewidth}{c|l} \toprule \large \textbf{Week or Date} & \large \textbf{Topics or whatever}\\ \midrule \midrule 1 & AI/ML overview\\ 2 & ML Evaluation\\ 3 & Linear Regression\\ 4 & Logistic Regression\\ 5 & Decision Tree\\ 6 & Neural Networks\\ 7 & Fall Break\\ 8 & Convolutional Neural Networks\\ 9 & Recurrent Neural Networks\\ 10 & Graph Neural Networks\\ 11 & Large Language Model\\ 12 & Recommendation System\\ 13 & Pruning and Quantization\\ 14 & Neural Architecture Search\\ 15 & Reinforcement Learning\\ 16 & Final Project\\ \bottomrule \end{tabularx} } \end{table}} \newcommand\gradingPolicy{\noindent Assignments - 40\%; \\ Midterm - 30\%; \\ Final - 30\%; \\ \\ Late submissions will be assessed a 10\% penalty per day. \\ Letter grade scale: A(94--100), A--(90--93), B+(87--89), B(83--86), B--(80--82), C+(77--79),\\ C(73--76), C--(70--72), D+(67--69), D(63--66), D--(60--62), F(<60)} \newcommand\HWandLabPolicy{} \newcommand\AIPolicy{\textcolor{red}{ Since you opted for an AI Policy, you should edit this part, choosing one of the following statements, modifying as desired:\\ \\ The use of AI-generated content is not permitted in this course. Its use will result in an academic integrity violation and a zero on the assignment.\\ \\ OR\\ \\ The use of AI-generated content is allowed in this course.\\ \\ OR\\ \\ The use of AI-generated content is permitted as follows: (a) for generating a first draft or (b) for generating an outline or (c) for generating XXX.\\ \\ AND, if AI is allowed:\\ \\ Even if you have significantly edited AI-generated material, you must identify the AI tool used to assist in generating your work. You are required to provide the name of the tool, date used, and prompts used to generate the output. As you may be required to submit the original AI output, you must keep a copy of the original output and provide it when requested. If questions arise about the authorship of submitted work, you are responsible for authenticating your authorship. 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