Elements of Machine Learning https://www.cs.duke.edu/courses/fall20/compsci 371d / Introduction and Logistics
A Penny for your Thoughts • What word best describes how you are feeling today? • What is your main concern as you start your semester? • Tell us all in the chat window
Machine Learning Applications • Data Security : Is this file malware? • Fraud Detection : Is this transaction money laundering? • Personal Security : What’s in your bag? Is that you? • Photo Collections : Here are all photos of Jenny playing tennis • Financial Trading : Is this trade likely to profit me? • Healthcare : Does this scan have a tumor? Do these symptoms suggest diabetes? • Marketing Personalization : What can I sell you ? What movies do you like? • Online Search : Why did/didn’t you like this search result? • Speech Processing : What did you say? Let me transfer your call • Natural Language Processing : Here is the information you need • Chatbots : I can help you with your order. Tell me more about your symptoms • Smart Cars : Are you comfortable? Are you alert? Stay in lane! Let me drive… • …
Machine Learning in One Slide • Identify a function y = f(x) : x = email, y = SPAM/NO SPAM • Give lots of examples (a training set): T = {( x 1 , y 1 ), …, ( x N , y N )} • A learner is another function λ : It takes T as input and outputs an approximation to f : h = λ ( T ) • Hopefully, f and h behave about the same even for previously unseen data : h ( x ) ≈ f ( x ) • That’s the big problem! • ML is not (just) data fitting
Logistics
Academic Integrity • Short version: Cheating will be prosecuted • Cheating: Using someone else’s material in your work without giving credit [Lone exception: class materials need not be cited] • Ditto for making materials available to others • Giver/receiver are treated the same • Format for using/making available is immaterial • Only communication allowed during homework is with your group peers, if any, and with the teaching sta ff
Your Weekly Schedule • Tuesday: For one brownie point, submit questions on current topic on Piazza by midnight EDT • Wednesday: Quiz on current topic due by midnight EDT Tuesday Midnight EDT Questions • Thursday: Wednesday Midnight EDT Quiz • Homework about previous topic Thursday 8 AM EDT Homework due by 8am EDT 8:30 AM or Thursday Discussion • Mandatory, synchronous 1:45 PM EDT discussion of current topic on Zoom at 8:30am or 1:45pm • For three brownie points, help answer one of the questions
Videos and Notes • Videos are full lectures, just edited for brevity • They will be posted in a media library on Warpwire, accessible through Sakai • Links to individual videos will also be posted on the syllabus page • Notes on the class Syllabus web page are required reading, and are your main source of information • All appendices in the notes are optional reading • Feel free to integrate with other sources. See Resources web page
Quizzes • Quizzes test basic knowledge from videos and notes • Each quiz is due on Wednesday midnight and is on topics discussed on Thursday • Quiz points add up to 120 and saturate at 100, score out of 100 • No late quizzes accepted • Two worst quiz scores (including 0s for no quiz) are dropped
Discussion Q & A • You attend one discussion session per week • Zoom meeting numbers on mechanics page and on Sakai. Must join from a Zoom account linked to a Duke email address • You may submit questions for discussion any time before the session • The first question you send by the rules and by the Tuesday midnight deadline earns you a brownie point • You are encouraged to upvote questions by others to determine order of discussion • Helping to answer questions during discussion earns you three brownie points • You can earn up to 10 brownie points over the semester • For full class participation score: min(10, 90-th percentile of points in class)
Zoom Etiquette • Please leave your video on if possible • Please mute yourself to avoid background noise. Unmute when talking (space bar for brief unmute) • Raise your hand to ask questions • Resist the strong temptation to sit on your hands: Engage!
Homework • One per topic • Some math, some text, some programming • OK to work in groups of one, two, three [but no division of labor!] • Jupyter notebooks → HTML → PDF • Keep Jupyter cells small • Two submissions on Gradescope: PDF , Notebook • One pair of submissions per group, remember to list all names! • No late homework accepted • Two worst homework scores (including 0s for no homework) are dropped
Exams and Grades • Exams: • One midterm on October 8, synchronous, at your section’s discussion time • One final, scheduling TBD, not cumulative • Submitted via Gradescope • Grades: • Homework 30%, Midterm 20%, Final 20%, Quizzes 15%, Participation (brownie points) 15% • Lowest two homework scores dropped • Points for each quiz add to 120, saturate at 100, out of 100. Lowest two quiz scores dropped
Programming • All programming will be in Python 3 (not 2!) • If you know how to program, picking up Python takes a few hours and Google while you program • If you don’t know how to program, this class may not be for you • You will write Jupyter Notebooks for homework. They are easy to get used to, and let you intersperse text, math, figures, and code • A first homework assignment will help you ease into these tools • The Anaconda distribution for everything you need is very strongly recommended • See the Resources web page for tutorials on Python 3, Jupyter, Anaconda
Teaching Staff • Graduate TAs : Kelsey Lieberman, Vinayak Gupta • Undergraduate TAs : Anna Darwish, Barbara Xiong, Bhrij Patel, Chaofan Tao, Janchao Geng, Kunal Upadya • If you like this course, please volunteer to TA next year! • Each of us will have Zoom o ffi ce hours per week, times TBA. O ffi ce hours can be group or individual as needed • Check the online calendar before attending o ffi ce hours • We’ll keep listening to Piazza (at reasonable hours) • Talk to us! We are here to help you learn
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