Introduction to CIS 419/519 Applied Machine Learning

Introduction to CIS 419/519 Applied Machine Learning

Introduction to CIS 419/519 Applied Machine Learning Dan Roth [email protected]|http://www.cis.upenn.edu/~danroth/|461C, 3401 Walnut Slides were created by Dan Roth (for CIS519/419 at Penn or CS446 at UIUC), Eric Eaton for CIS519/419 at Penn, or from other authors who CIS 419/519 Fall19 have made their ML slides available. 1 CIS 419/519: Applied Machine Learning Monday, Wednesday: 10:30pm-12:00pm 101 Levine

Starting next week Office hours: Mon/Tue 5-6 pm [my office] TAs Office Hours will also start next 10 TAs week Assignments: 5 Problems set (Python Programming) Weekly (light) on-line quizzes HW0 !!! Weekly Discussion Sessions Go to the web site Mid Term Exam [Project] (look at the schedule)Be on Piazza Registration for

Final Class No real textbook: Slides/Mitchell/Flach/Other Books/ Lecture notes /Literature CIS 419/519 Fall19 2 CIS 419/519: Today What is Learning? Who are you? What is CIS 419/519 about? The Badges Game CIS 419/519 Fall19 3

An Owed to the Spelling Checker I have a spelling checker, it came with my PC It plane lee marks four my revue Miss steaks aye can knot sea. Eye ran this poem threw it, your sure reel glad two no. Its vary polished in it's weigh My checker tolled me sew. A checker is a bless sing, it freeze yew lodes of thyme. It helps me right awl stiles two reed And aides me when aye rime. Each frays come posed up on my screen Eye trussed to bee a joule...

CIS 419/519 Fall19 4 Machine Learning is Everywhere CIS 419/519 Fall19 5 Applications: Spam Detection This is a binary classification task: Assign one of two labels (i.e. yes/no) to the input (here, an email message) Classification requires a model (a classifier) to determine which label to assign to items. In this class, we study algorithms and techniques to learn such models from

data. CIS 419/519 Fall19 Documents Labels Documents Finance Sentences Negative Phrases Images Snakes Medical records soon/Not

Politics, Sports, Positive, Person, Location Cats, Dogs, Admit again 6 ? Machine Learning: Any Limitations? CIS 419/519 Fall19 7 Comprehension (ENGLAND, June, 1989) - Christopher Robin is alive and well. He lives in England. He is the same person that you read about in the book, Winnie the Pooh. As a boy, Chris lived in a pretty home called Cotchfield Farm. When Chris was three years old, his father wrote a poem about him. The poem was printed in a magazine for others to read. Mr. Robin then wrote a book. He

made up a fairy tale land where Chris lived. His friends were animals. There was a bear called Winnie the Pooh. There was also an owl and a young pig, called a piglet. All the animals were stuffed toys that Chris owned. Mr. Robin made them come to life with his words. The places in the story were all near Cotchfield Farm. Winnie the Pooh was written in 1925. Children still love to read about Christopher Robin and his animal friends. Most people don't know he is a real person who is grown now. He has written two books of his own. They tell what it is like to be 1.famous. Christopher Robin was born in England. 2. Winnie the Pooh is a title of a book. 3. Christopher Robins dad was a magician. 4. Christopher Robin This is an Inference Problem; where is the must be at least 65 now.

CIS 419/519 Fall19 learning? 8 CIS 419/519 Fall19 9 Learning Learning is at the core of Understanding High Level Cognition Performing knowledge intensive inferences Building adaptive, intelligent systems Dealing with messy, real world data Analytics

Learning has multiple purposes Knowledge Acquisition Integration of various knowledge sources to ensure robust behavior Adaptation (human, systems) Decision Making (Predictions) CIS 419/519 Fall19 10 Learning = Generalization H. Simon Learning denotes changes in the system that are adaptive in the sense that they enable the system to do the task or tasks drawn from the same population more efficiently and more effectively the next time. The ability to perform a task in a situation which has never been encountered before CIS 419/519 Fall19 11 Learning = Generalization The learner has to be able to classify

items it has never seen before. Mail thinks this message is about my Fall 2018 ML Class CIS 419/519 Fall19 12 Learning = Generalization The ability to perform a task in a situation which has never been encountered before Classification Medical diagnosis; credit card applications; hand-written letters; ad selection; sentiment assignment, Planning and acting The a Badges Game playing (chess, backgammon, go); driving

car game Skills (A robot) balancing a pole; playing tennis Common sense reasoning What does the algorithm gets as input? (features) Natural language interactions CIS 419/519 Fall19 Generalization depends on the Representation as much as it depends on the Algorithm 13 Same Population? New Zealand In New York State, the longest period of daylight

occurs during the month of _________. CIS 419/519 Fall19 14 Why Study Machine Learning? A breakthrough in machine learning would be worth ten Microsofts -Bill Gates, Chairman, Microsoft Machine learning is the next Internet -Tony Tether, Former Director, DARPA Machine learning is the hot new thing -John Hennessy, President, Stanford Web rankings today are mostly a matter of machine learning -Prabhakar Raghavan, Dir. Research, Yahoo Machine learning is going to result in a real revolution -Greg Papadopoulos, CTO, Sun Machine learning is todays discontinuity -Jerry Yang, CEO, Yahoo CIS 419/519 Fall19 15 Why Study Learning?

Computer systems with new capabilities. AI Understand human and biological learning Understanding teaching better. Time is right. Initial algorithms and theory in place. Growing amounts of on-line data Computational power available. Necessity: many things we want to do cannot be done by programming. (Think about all the examples given earlier) CIS 419/519 Fall19

16 Learning is the Future Learning techniques will be a basis for every application that involves a connection to the messy real world Basic learning algorithms are ready for use in applications today Prospects for broader future applications make for exciting fundamental research and development opportunities Many unresolved issues Theory and Systems While learning is hot, there are many things we dont know how to do And thats why this is NOT a class about deep neural networks Very active field What to teach? And: what we dont The fundamental paradigms know Some of the most important algorithmic ideas Modeling CIS 419/519 Fall19

17 US Open Highlights Over 700 matches over two weeks periods. A lot of manpower required to generate video highlights. Key: automate using incidental signals Crowd noises, like gasps and cheers. Players gestures and reactions (e.g. celebratory air punches and fist pumps) Non-trivial: need to deal with biases, etc. CIS 419/519 Fall19 18 https://venturebeat.com/2019/07/05/how-wimbledon-and-watson-are-using-ai-to-curate-video-highlights/ Course Overview Introduction: Basic problems and questions A detailed example: Linear classifiers; key algorithmic idea Two Basic Paradigms: Discriminative Learning & Generative/Probabilistic Learning

Learning Protocols: Supervised; Unsupervised; Semi-supervised Algorithms Gradient Descent Decision Trees Linear Representations: (Perceptron; SVMs; Kernels) Neural Networks/Deep Learning Probabilistic Representations (nave Bayes) Unsupervised /Semi supervised: EM Clustering; Dimensionality Reduction Modeling; Evaluation; Real world challenges Ethics CIS 419/519 Fall19

19 CIS 419/519: Applied Machine Learning Monday, Wednesday: 10:30pm-12:00pm 101 Levine Starting next week Office hours: Mon/Tue 5-6 pm [my office] TAs Office Hours will also start next 10 TAs week Assignments: 5 Problems set (Python Programming) Weekly (light) on-line quizzes

HW0 !!! Weekly Discussion Sessions Go to the web site Mid Term Exam [Project] (look at the schedule)Be on Piazza Registration for Final Class No real textbook: Slides/Mitchell/Flach/Other Books/ Lecture notes /Literature CIS 419/519 Fall19 20 CIS 519: What have you learned so far? What do you need to know:

Some exposure to: Theory of Computation Probability Theory Linear Algebra Participate, Ask Questions Ask during class, not after class Applied Machine Learning Applied: mostly in HW

Machine learning: mostly in class, quizzes, exams Programming (Python) Homework 0 If you could not comfortably deal with 2/3 of this within a few hours, please take the prerequisites first; come back next semester/year. CIS 419/519 Fall19 21 CIS 519: Policies Cheating No. We take it very seriously. Class Web Page Note also the Schedule Page and our Notes Homework:

Collaboration is encouraged But, you have to write your own solution/code. Late Policy: You have a credit of 4 days; Thats it. Grading: Possible separate for grad/undergrads. 40% - homework; ; 35%-final; 20%-midterm; 5% Quizzes [Projects: 20%] Questions? CIS 419/519 Fall19 22 CIS 519 on the web Check our class website: Schedule, slides, videos, policies http://www.seas.upenn.edu/~cis519/fall2019/ Sign up, participate in our Piazza forum: Announcements and discussions http://piazza.com/upenn/fall2019/cis419519

Check out our team Office hours [Optional] Discussion Sessions CIS 419/519 Fall19 23 What is Learning? The Badges Game This is an example of the key learning protocol: supervised learning First question: Are you sure you got it? Why? Issues:

Prediction or Modeling? Representation Problem setting Background Knowledge When did learning take place? Algorithm CIS 419/519 Fall19 24

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