Lecture 1: Course outline and logistics What is Machine Learning. Aykut Erdem February 2016 Hacettepe University

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1 Lecture 1: Course outline and logistics What is Machine Learning Aykut Erdem February 2016 Hacettepe University

2 Today s Schedule Course outline and logistics An overview of Machine Learning 2

3 Course outline and logistics

Logistics Instructor: Aykut ERDEM (aykut@cs.hacettepe.edu.tr) Teaching Assistant: Aysun Kocak (aysunkocak@cs.hacettepe.edu.tr) Burcak Asal (basal@cs.

4 Logistics Instructor: Aykut ERDEM Teaching Assistant: Aysun Kocak Burcak Asal Lectures: Tue 10:00-10:50_D10 Thu 09:00-10:50_D9 Tutorials: Fri 09:00-10:50_D8 4

5 About this course This is a undergraduate-level introductory course in machine learning (ML) A broad overview of many concepts and algorithms in ML. Requirements Basic algorithms, data structures. Basic probability and statistics. Basic linear algebra and calculus Good programming skills common distributions, Bayes rule, mean/median/model vector/matrix manipulations, partial derivatives BBM 409 Introduction to Machine Learning Practicum (New) Students will gain skills to apply the concepts to real world problems. 5

6 Communication The course webpage will be updated regularly throughout the semester with lecture notes, programming and reading assignments and important deadlines. spring2016/bbm406/ We will be using Piazza for course related discussions and announcements. Please enroll the class on Piazza by following the link 6

Reference Books Artificial Intelligence: A

(online version available) Introduction to

7 Reference Books Artificial Intelligence: A Modern Approach (3rd Edition), Russell and Norvig. Prentice Hall, 2009 Bayesian Reasoning and Machine Learning, Barber, Cambridge University Press, (online version available) Introduction to Machine Learning (2nd Edition), Alpaydin, MIT Press, 2010 Pattern Recognition and Machine Learning, Bishop, Springer, 2006 Machine Learning: A Probabilistic Perspective, Murphy, MIT Press,

8 Grading Policy Grading for BBM 406 will be based on a course project (done in pairs) (25%), a midterm exam (30%), a final exam (40%), and class participation (5%) In BBM 409, the grading will be based on a set of quizzes (20%), and 3 assignments (done individually) 8

9 Assignments 3 assignments, first one worth 20%, last two worth 30% each Theoretical: Pencil-and-paper derivations Programming: Implementing Python code to solve a given real-world problem A quick Python tutorial in this week s tutorial session. 9

10 10

11 Course Project Done individually, or in teams of two students. Choose your own topic and explore ways to solve the problem Proposal: 1 page (Mar 8) (10%) Progress Report: 4-5 pages (Apr 19) (25%) Poster Presentation: (last week of classes) (20%) Final Report: (due at the beginning of poster session) (45%) 11

12 Collaboration Policy All work on assignments have to be done individually. The course project, however, can be done in pairs. You are encouraged to discuss with your classmates about the given assignments, but these discussions should be carried out in an abstract way. In short, turning in someone else s work, in whole or in part, as your own will be considered as a violation of academic integrity. Please note that the former condition also holds for the material found on the web as everything on the web has been written by someone else. 12

13 Course Outline Week1 Overview of Machine Learning, Nearest Neighbor Classifier Week2 Linear Regression, Least Squares Week3 Machine Learning Methodology Assg1 out Week4 Statistical Estimation: MLE, MAP, Naïve Bayes Classifier Week5 Linear Classification Models: Logistic Regression, Linear Discriminant Functions, Perceptron Week6 Neural Networks Week7 Midterm Exam Assg1 due, Assg2 out Course project proposal due Assg2 due Assg3 out 13

14 Course Outline (cont d.) Week8 Deep Learning Week9 Support Vector Machines (SVMs) Week10 Multi-class SVM Assg3 due Week11 Decision Tree Learning Week12 Ensemble Methods: Bagging, Random Forests, Boosting Week13 Clustering Project progress report due Week14 Principle Component Analysis, Autoencoders 14

15 Machine Learning: An Overview

16 Quotes If you were a current computer science student what area would you start studying heavily? Answer: Machine Learning. The ultimate is computers that learn Bill Gates, Reddit AMA Machine learning is the next Internet Tony Tether, Director, DARPA Machine learning is today s discontinuity slide by David Sontag Jerry Yang, CEO, Yahoo 16

17 Google Trends Machine learning Deep learning 17

18 2015 Edition

19 2016 Edition

20 Learning slide by Bernhard Schölkopf Richard Feynman 20

21 Two definitions of learning (1) Learning is the acquisition of knowledge about the world. Kupfermann (1985) (2) Learning is an adaptive change in behavior caused by experience. slide by Bernhard Schölkopf (1988) Shepherd 21

22 Empirical Inference Drawing conclusions from empirical data (observations, measurements) Example1: Scientific inference y = Σ i a i k(x,x i ) + b y x x x x y = a * x slide by Bernhard Schölkopf x x x Leibniz, Weyl, Chaitin x x x 8 Bernhard Schölkopf 22

23 Empirical Inference Example2: Perception slide by Bernhard Schölkopf 23

24 slide by Bernhard Schölkopf

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44 Empirical Inference Example2: Perception "The brain is nothing but a sta0s0cal decision organ" H. Barlow slide by Bernhard Schölkopf 44

45 slide by Bernhard Schölkopf X

46 slide by Bernhard Schölkopf X

48 slide by Bernhard Schölkopf

49 What is machine learning?

50 Example: Netflix Challenge Goal: Predict how a viewer will rate a movie 10% improvement = 1 million dollars slide by Yaser Abu-Mostapha 50

51 Example: Netflix Challenge Goal: Predict how a viewer will rate a movie 10% improvement = 1 million dollars Essence of Machine Learning: A pattern exists We cannot pin it down mathematically We have data on it slide by Yaser Abu-Mostapha 51

52 Watch out AlphaGo vs. Lee Sedol in March! 52

53 Comparison Traditional Programming Data Program Computer Output slide by Pedro Domingos, Tom Mitchel, Tom Dietterich Machine Learning Data Computer Output Program 53

54 What is Machine Learning? [Arthur Samuel, 1959] Field of study that gives computers the ability to learn without being explicitly programmed [Kevin Murphy] algorithms that automatically detect patterns in data use the uncovered patterns to predict future data or other outcomes of interest [Tom Mitchell] algorithms that improve their performance (P) slide by Dhruv Batra at some task (T) with experience (E) 54

55 What is Machine Learning? If you are a Scientist Data Machine Learning Understanding slide by Dhruv Batra If you are an Engineer / Entrepreneur Get lots of data Machine Learning??? Profit! 55

56 Why Study Machine Learning? Engineering Better Computing Systems Develop systems too difficult/expensive to construct manually because they require specific detailed skills/knowledge knowledge engineering bottleneck Develop systems that adapt and customize themselves to individual users. Personalized news or mail filter Personalized tutoring Discover new knowledge from large databases slide by Dhruv Batra Medical text mining (e.g. migraines to calcium channel blockers to magnesium) data mining 56

57 Why Study Machine Learning? Cognitive Science Computational studies of learning may help us understand learning in humans and other biological organisms. Hebbian neural learning Neurons that fire together, wire together. slide by Dhruv Batra 57

58 Why Study Machine Learning? The Time is Ripe Algorithms Many basic effective and efficient algorithms available. Data Large amounts of on-line data available. Computing Large amounts of computational resources available. slide by Ray Mooney 58

59 Where does ML fit in? slide by Fei Sha 59

60 A Brief History of AI slide by Dhruv Batra 60

61 adopted from Dhruv Batra 61

62 AI Predictions: Experts slide by Dhruv Batra Image Credit: 62

63 AI Predictions: Non-Experts slide by Dhruv Batra Image Credit: 63

64 AI Predictions: Failed slide by Dhruv Batra Image Credit: 64

65 Why is AI hard? slide by Dhruv Batra Image Credit: 65

66 What humans see slide by Larry Zitnick 66

67 What computers see slide by Larry Zitnick 67

68 I saw her duck slide by Liang Huang Image Credit: Liang Huang 68

69 I saw her duck slide by Liang Huang Image Credit: Liang Huang 69

70 I saw her duck slide by Liang Huang Image Credit: Liang Huang 70

We ve come a long way IBM Watson What is Jeopardy? http://youtu.be/xqb66bdsqlw?t=53s Challenge: http://youtu.be/_429uizn1jm Watson Demo: http://youtu.be/wfr3lom_xhe?t=22s Explanation http://youtu.

71 We ve come a long way IBM Watson What is Jeopardy? Challenge: Watson Demo: Explanation IBM Watson wins on Jeopardy (February 2011) Watson provides cancer treatment options to doctors in seconds (February 2013) slide by Liang Huang Future: Automated operator, doctor assistant, finance 71

72 Why are things working today? More compute power Better More data Better algorithms/ models Accuracy slide by Dhruv Batra Amount of Training Data Figure Credit: Banko & Brill,

Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition

Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition Todd Holloway Two Lecture Series for B551 November 20 & 27, 2007 Indiana University Outline Introduction Bias and