Introduc7on$&$Overview$ · CIS$520:$Machine$Learning$ $ Introduc7on$&$Overview$ $ Shivani$Agarwal$ $ Departmentof$Computer$&$Informaon$Science$ University$of$Pennsylvania $ $ Spring2019$

CIS 520: Machine Learning

Introduc7on & Overview

Shivani Agarwal

Department of Computer & Informa7on Science University of Pennsylvania

Spring 2019

Example 1: Email Spam Filter •  Would like to build an email

filter that can predict whether a new message is spam or non-‐spam

•  Have data containing previous examples of messages labeled as spam or non-‐spam

•  Can we “learn” an email filter from this data?

Shivani Agarwal CIS 520: Machine Learning Spring 2019

Example 2: HandwriTen Digit Recogni7on •  Would like to build a model

that can automa7cally recognize handwriTen digits from images

•  Have data containing examples of such images labeled with the correct digit (0,1,…,9)

•  Can we “learn” an accurate recogni7on model from this data?


Example 3: Weather Forecas7ng

•  Would like to build a forecas7ng model which, given a satellite image showing water vapor in a region, can predict the amount of rainfall in the coming week

•  Have data containing examples of such images recorded in the past, together with the amount of rainfall observed in the following weeks

•  Can we “learn” a forecas7ng model from this data?


Example 4: Gene Expression Analysis •  Would like to iden7fy gene7c

paTerns in pa7ents, such as groups of genes that have similar behavior, or groups of pa7ents that have a similar form of gene7c disease

•  Have microarray expression data containing expression levels of thousands of genes in various pa7ents

•  Can we “learn” gene7c paTerns from this data?


Example 5: Natural Language Parsing •  Would like to build a natural

language parser which, given an English sentence, can predict the correct parse tree for the sentence

•  Have data containing examples of English sentences with their correct parse tree annota7ons

•  Can we “learn” a natural language parser from this data?

The cat threw the ball.

DET DET N N V

NP NP

VP

S


Example 6: Movie Recommenda7ons

•  Would like to build a recommenda7on system that can recommend movies to users

•  Have a (highly incomplete) movie ra7ng matrix containing ra7ngs (1-‐5 stars) provided by various users for movies they have watched

•  Can we “learn” a recommenda7on system from this data?

2 5 3 1

4

4

4 2

3

3 5 4 1

5 2 4

u1 u2

um

m1 m2 mn


Why Study Machine Learning?

Wouldn’t it be fun to see to what extent human learning can be implemented in machines, and in the process, perhaps gain insights into how humans learn in the first place?



Lots of cool math. Lots of rich theory. Applica7ons too!



I can use my knowledge of computer science to help design algorithms that can learn from data in almost every field. I can finally show my friends in astronomy, biology, finance, … how cool computer science is!



Wow! I can use machine learning methods to automa&cally discover paTerns in my data and use these to inform decisions!


Data Scientist


•  Predic7ve models needed in many areas of science, engineering, business

•  Data everywhere: astronomy, biology, climate modeling, drug discovery, finance, geology, Web,…

•  Would like to understand how to design and analyze algorithms that can automa7cally “learn” predic7ve models from this data


Aeer Taking this Course, You Will…

•  Have a strong founda7on in machine learning •  Be able to apply machine learning methods to real-‐

life problems, as well as modify/design new algorithms where needed

•  Be prepared for advanced coursework/research in machine learning and related fields


Overview of Some Logis7cs


Course Website

hTp://www.shivani-‐agarwal.net/Teaching/

CIS-‐520/Spring-‐2019/index.html


(Check regularly for schedule updates, lecture notes, addi7onal references/pointers, etc!)

Prerequisites

– Probability (ESE 301 / STAT 510/430 / ENM 503) – Linear Algebra (EAS 205 / MATH 312) – Algorithms & Complexity (Big-‐O nota7on etc) – Programming Experience


Textbooks Primary Reading: •  Lecture notes/occasionally other materials (available online) Recommended Textbook: •  C.M. Bishop, Pa+ern Recogni&on and Machine Learning. Springer,

2006. Addi>onal Textbooks: •  T. Has7e, R. Tibshirani and J. Friedman, The Elements of Sta&s&cal

Learning: Data Mining, Inference and Predic&on. Springer, 2nd Edi7on, 2009.

•  R.O. Duda, P.E. Hart and D.G. Stork. Pa+ern Classifica&on. Wiley-‐Interscience, 2nd Edi7on, 2000.

•  T. Mitchell, Machine Learning. McGraw Hill, 1997.


Soeware

MATLAB 2017A

Download free copy:

hTps://www.seas.upenn.edu/cets/soeware/matlab/student/

MATLAB tutorial:

hTps://alliance.seas.upenn.edu/~cis520/dynamic/2017/wiki/index.php?n=Recita7ons.MatlabTutorial


Problem Set Submissions

LaTeX


Grading

6 Problem Sets 24% Class Par7cipa7on 6% Midterm Exam (Tue Feb 26) 20% Project 25% Final Exam 25%


Course Policies (Details on Webpage)

•  Problem Sets: –  You can work in groups, but you must write your own code and solu7ons.

–  Up to 2 late submissions, late by up to 2 days each. •  Projects: – Work must be your own (teams of 3-‐4). –  No late submissions.

•  Exams: – Make-‐up exams only in extenua&ng circumstances; submit requests at least one week in advance.


Course Policies (Details on Webpage)

•  Academic Honesty: –  Don’t copy. Don’t allow anyone to copy. –  If you have difficulty: Form study groups. Bounce off ideas with one another. Teach each other what you understand. Come to our office hours.

–  If any part of a problem set/project/exam is found to be copied, it will automa7cally result in a zero grade for the en&re problem set/project/exam (for both the person copying and any person allowing his/her work to be copied), a warning note to your academic advisor, and a referral to the Office of Student Conduct. Any repeat instance will automa7cally lead to a failing grade in the course.


Teaching Assistants


Mohammad Fereydounian (Head TA)

Haoxian Chen Jane Lee

Simeng Sun Arnab Sarker Rohan Menezes

Brandon Lin

Edward (Barry) PlunkeP

Reaching Out to Us

•  Office Hours: –  Times/loca7ons coming soon (check course website)

•  Piazza: –  Use for all course-‐related communica7on with us (no email; can post privately/anonymously on Piazza if desired)

–  Use for ques7ons that can be answered in <5 mins, and for discussions amongst yourselves; help each other out! (good answers count toward class par7cipa7on)

•  Canvas: –  Use for problem set/project submissions and grades only.


Let’s Get Started!


Documents

Introduc7on$&$Overview$ · CIS$520:$Machine$Learning$ $ Introduc7on$&$Overview$ $ Shivani$Agarwal$ $ Departmentof$Computer$&$Informaon$Science$ University$of$Pennsylvania $ $ Spring2019$