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ENGG*6500Introduction to Machine LearningGeneral InformationHandout“1st Meeting: Thursday, May 12th, 2022”Summer 2022
Shawki AreibiOffice, Email, Phone Office: Thorn 2335, EXT 53819 Email: sareibi@uoguelph.caWeb Page: https://sareibi.uoguelph.ca/Webex: https://uoguelph.webex.com/meet/sareibiOffice Hours: (Friday) 3:00 PM – 4:00 PMPhD, Waterloo 1995Research Interests VLSI Physical Design Automation (CAD/EDA)2 Combinatorial Optimization (Heuristics/Meta-heuristics) Reconfigurable Computing Systems (RCS)/Embedded Systems Using Machine Learning/Deep Learning within RCS/EDAML/DL Summer 2022
Outline 3Lecture ScheduleStaff (GTA, Lab Tech)Course Text and ReferencesResources and CommunicationAssignments, ProjectEvaluationCourse contents, Tentative ScheduleML/DL Summer 2022
Lecture ScheduleoLectures 14:30 PM – 16:00 PM (Tue/Thur)oo MCKN 224We might change room to RICH 2529Or Online using Webex oAssignments/Projects: 4https://uoguelph.webex.com/meet/sareibiSubmit via CourselinkSubmit on due date givenML/DL Summer 2022
Teaching Assistance (GTA) Peyman Tahghighi PhD Student (Biomedical Engineering) Email: ptahghig@uoguelph.ca Thornbrough Building Office: 2319 ext. xxx Tasks: Tutorials on Scikit Learn if necessary Marking Assignments, Projects5ML/DL Summer 2022
Lab Instructor/Tech Manager Matthew Kent System Administrator Email: mattkent@uoguelph.ca Thornbrough Building, Room 2332 ext. 54113 Matthew Kurylo Information Technology Specialist Email: mkurylo@uoguelph.ca Thornbrough Building, Room 2363, ext. 52685Preferred email: soeithelp@uoguelph.ca6ML/DL Summer 2022
Textbook and ReferencesTextbooks & References1.2.3.4.5.7 Introduction to Machine Learning'', by Etherm Alpaydin. MITPress, 2020, 4th Edition, ISBN 0387310738. Hands-on Machine Learning with Scikit-Learn, Keras, andTensorFlow'', by A.n Geron, O'Reilly Media Inc, 20019, ISBN 978-14020-6088-5. A Course in Machine Learning", by Hal Daume III, 2017. Deep Learning", by I. Goodfellow, Y. Bengio, A. Courville, MITPress, 2016, freely available online. Machine Learning: A Probabilistic Perspective", by Kevin P.Murphy, MIT Press, 2012.ML/DL Summer 2022
Resources & Communication Courselink:ooo Course Web Page:o Dropbox for Assignments and Project.Lecture ibi/TEACHING dr/ENG6500 ML html dr/eng6500-ml.htmlCommunications1. E-mail, Courselink (Announcements)8ML/DL Summer 2022
PrerequisitesoImportant PrerequisitesooooHelpful but not required:oo9Linear AlgebraCalculasProgramming languages: Python (C, C )OptimizationAIML/DL Summer 2022
Course Goals, ObjectivesAchieves the following goals:1.2.3.4.5.10Recognize the most common machine learning paradigms:classification, regression, density estimation, clustering e.t.cImplement base Machine Learning Algorithms andunderstand the basic underlying strategy.Debug, visualize, and evaluate a running algorithm.Identify and discuss general machine learning concepts suchas generalization, overfitting, preprocessing, featureextraction, evaluation, training/validation/test setsWhen appropriate, choose among and apply off-the-shelfmachine learning toolkits to problems in your field.ML/DL Summer 2022
Evaluation Criteria11TopicWeightAssignments25%Week 2-9Project35%Week 8-12Final Exam40%ML/DL Summer 2022DetailsWeek #13
Assignments4-6 �––Week#3, Data PreparationWeek#4, RegressionWeek#5, ClassificationWeek#7, ClusteringWeek#9, Feature Selection/Eval.Week#10, Ensemble Based Algs.ML/DL Summer 2022
Research Projecto “Graduate Students” will select a topic related toMachine Learning.o Every two students will form a group and submita single final report.o You should conduct an in-depth study coveringthe problem to be solved and its current status.o Your finding should be documented in a report:1.2.3.4.13Introduction & MotivationBackground, Literature ReviewMethodology, ResultsConclusionML/DL Summer 2022
Sample Projects Projects can be either: Supervised Learning basedo Regression,o Classification Unsupervised Learning:o Clustering.o Feature Reduction. A proposal should be written and submitted Code should be submitted with the report14ML/DL Summer 2022
Tentative cTopicTopicTopicTopicTopic#1, Overview of AI and Machine Learning (ML)#2, Overview of Python and ML Frameworks#3, Optimization Techniques for Machine Learning#4, Data Preparation and Feature Extraction#5, Evaluation of Regression/Classification#6, Supervised Machine Learning Algorithms#7, Unsupervised Machine Learning Algorithms#8, Ensemble Based Machine Learning#9, Loss Functions and Parameters#10, Machine Learning Applications#11, Intro. To Deep Learning and Deep NetworksML/DL Summer 2022
Expectationsoooooooooo16Attending Classes.Understanding concepts introduced in class.Participating in all class discussions.Ask Questions if in doubt.Complete Assignments on time.Read your email and check announcements.Follow instructions (assignments, project, exam).Reading material and resources made available.Go beyond the lecture material.Follow academic integrity code.ML/DL Summer 2022
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o Topic #1, Overview of AI and Machine Learning (ML) o Topic #2, Overview of Python and ML Frameworks o Topic #3, Optimization Techniques for Machine Learning o Topic #4, Data Preparation and Feature Extraction o Topic #5, Evaluation of Regression/Classification o Topic #6, Supervised Machine Learning Algorithms o Topic #7, Unsupervised Machine .
