CS 6140: Machine Learning Spring 2017

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1 CS 6140: Machine Learning Spring 2017 Instructor: Lu Wang College of Computer and Science Northeastern University Webpage:

2 Time and Time: Thursdays from 6:00 pm 9:00 pm Loca)on: Forsyth Building 129

3 Course Webpage hpp:// courses/cs6140_sp2017.html

4 Prerequisites Programming Being able to write code in some programming languages (e.g. Python, Java, C/C++, Matlab) proficiently Courses Algorithms Probability and Linear algebra

5 Prerequisites Courses Algorithms Probability and Linear algebra A quiz: 22 simple ques@ons, 20 of them as True or False ques@ons (relevant to probability, sta@s@cs, and linear algebra) The purpose of this quiz is to indicate the expected background of students. 80% of the ques@ons should be easy to answer. Not counted in your final score!

6 Textbook and References Main Textbook Kevin Murphy, "Machine Learning - a Probabilis@c Perspec@ve", MIT Press, Christopher M. Bishop, "PaPern Recogni@on and Machine Learning", Springer, Other textbooks Tom Mitchell, "Machine Learning", McGraw Hill, Machine learning lectures

7 Content of the Course Regression: linear regression, regression Dimensionality Reduc)on: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis Probabilis)c Models: Naive Bayes, maximum likelihood Sta)s)cal Learning Theory: VC dimension Kernels: Support Vector Machines (SVMs), kernel tricks, duality Sequen)al Models and Structural Models: Hidden Markov Model (HMM), Random Fields (CRFs) Clustering: spectral clustering, hierarchical clustering Latent Variable Models: K-means, mixture models, (EM) algorithms, Latent Dirichlet (LDA), learning Deep Learning: feedforward neural network, restricted Boltzmann machine, autoencoders, recurrent neural network, neural network Reinforcement Learning: Markov decision processes, Q-learning and others, including advanced topics for machine learning in natural language processing and text analysis

8 The Goal understanding of machine learning models How to apply and design learning methods for novel problems

9 The Goal Not only what, but also why!

10 Grading Assignment 3 assignments, 10% for each Quiz 10 in-class tests, 1% for each Exam 1 exam, 30% Project 1 project, 27% Par@cipa@on 3% Classes Piazza

11 Exam Open book April 20, 2017

12 Course Project A machine learning relevant research project 2-3 students as a team

13 Topics Machine learning relevant Natural language processing Computer vision Health

14 Course Project Grading We want to see novel and projects! The problem needs to be well-defined, novel, useful, and machine learning techniques Reasonable results and

15 Project from Last Year

16 Project from Last Year Follow-back Behavior in Instagram Users

17 Project from Last Year Grasp Points Using Neural Networks

18 Project from Last Year Neural Networks for Drug Response in Tailored Therapy

19 Project from Last Year Threat from TwiPer

20 Project from Last Year Player Ranking in Popular Games

21 Course Project Grading Three reports Proposal (2%) Progress, with code (10%) Final, with code (10%) One In class (5%)

22 Submission and Late Policy Each assignment or report, both electronic copy and hard copy, is due at the beginning of class on the corresponding due date. Programming language Python, Java, C/C++, Matlab Electronic version On blackboard Hard copy In class

23 Submission and Late Policy Assignment or report turned in late will be charged 10 points (out of 100 points) off for each late day (i.e. 24 hours). Each student has a budget of 5 days throughout the semester before a late penalty is applied.

24 How to find us? Course webpage: hpp:// cs6140_sp2017.html Office hours Lu Wang: Thursdays from 4:30pm to 5:30pm, or by appointment, 448 WVH Rui Dong (TA), Tuesdays from 4:00pm to 5:00pm, or by appointment, 466B WVH Piazza hpp://piazza.com/northeastern/spring2017/cs All course relevant go here

25 What is Machine Learning? A set of methods that can automa@cally detect paperns in data, and then use the uncovered paperns to predict future data, or to perform other kinds of decisions making under certainty.

26 Real World

27 Real World

28 Real World

29 Real World

30 Real World

31 Real World

32 Real World

33 Real World

34 Real World

35 Real World

36 with Other Areas Natural Language Processing Computer Vision A lot of other areas

37 Today s Outline Basic concepts in machine learning K-nearest neighbors Linear regression Ridge regression

38 Supervised vs. Unsupervised Learning

39 Supervised Learning

40 Supervised vs. Unsupervised Learning Supervised learning Training set Training sample Gold-standard label - Classifica)on, if categorical - Regression, if numerical

41 Supervised Learning

42 Supervised Learning

43 Supervised Learning Goal: Generalizable to new input samples Overfivng vs. underfivng One we use models Typical setup: Step 1: Features Step 2: Training set, test set, development set Step 3:

44 Supervised Learning

45 Supervised Learning

46 Supervised Learning

47 Supervised Learning Regression stock price temperature revenue

48 Supervised vs. Unsupervised Learning Unsupervised Learning More about knowledge discovery

49 Unsupervised Learning Dimension Principal component analysis

50 Unsupervised Learning Clustering (e.g. graph mining) RolX: Role Extrac.on and Mining in Large Networks, by Henderson et al, 2011

51 Unsupervised Learning Topic modeling

52 Parametric vs. Non-parametric model Fixed number of parameters? If yes, parametric model Number of parameters grow with the amount of training data? If yes, non-parametric model tractability

53 Today s Outline Basic concepts in machine learning K-nearest neighbors Supervised learning A non-parametric classifier Linear regression Ridge regression

54 A non-parametric classifier: K-nearest neighbors (KNN)

55 A non-parametric classifier: K-nearest neighbors (KNN) Basic idea: memorize all the training samples The more you have in training data, the more the model has to remember

56 A non-parametric classifier: K-nearest neighbors (KNN) Basic idea: memorize all the training samples The more you have in training data, the more the model has to remember Nearest neighbor (or 1-nearest neighbor): phase: find closet sample, and return corresponding label

57

58

59 A non-parametric classifier: K-nearest neighbors (KNN) Basic idea: memorize all the training samples The more you have in training data, the more the model has to remember K-Nearest neighbor: phase: find the K nearest neighbors, and return the majority vote of their labels

60

61 About K K=1: just piecewise constant labeling K=N: global majority vote (class)

62 Problems of knn Can be slow when training data is big Searching for the neighbors Needs lots of memory to store training data Needs to tune k and distance func@on Not a probability distribu@on

63 Problems of knn Distance Euclidean distance

64 Problems of knn Distance Mahalanobis distance: weights on components

65 knn We prefer a probabilis@c output because some@mes we may get an uncertain result 1 samples as yes, 199 samples as no à? 99 samples as yes, 101 samples as no à? Probabilis@c knn:

66 knn 3-class training data

67 Smoothing Class 1: 3, class 2: 0, class 3: 1 Original probability: P(y=1)=3/4, p(y=2)=0/4, p(y=3)=1/4

68 Smoothing Class 1: 3, class 2: 0, class 3: 1 Original probability: P(y=1)=3/4, p(y=2)=0/4, p(y=3)=1/4 Add-1 smoothing: Class 1: 3+1, class 2: 0+1, class 3: 1+1 P(y=1)=4/7, p(y=2)=1/7, p(y=3)=2/7

69 Soxmax Class 1: 3, class 2: 0, class 3: 1 Original probability: P(y=1)=3/4, p(y=2)=0/4, p(y=3)=1/4 Redistribute probability mass into different classes Define a soxmax as

70 Today s Outline Basic concepts in machine learning K-nearest neighbors Linear regression Supervised learning A parametric classifier Ridge regression

71 A parametric classifier: linear regression the response is a linear func@on of the inputs Inner product between input sample X and weight vector W Residual error: difference between predic@on and true label

72 A parametric classifier: linear regression Inner product between input sample X and weight vector W Residual error: difference between predic@on and true label Assume residual error has a normal distribu@on

73 A parametric classifier: linear regression We can further assume Basic expansion

74 A parametric classifier: linear regression temperature Horizontal: within a room

75 A parametric classifier: linear regression

76 Learning with Maximum Likelihood (MLE) Maximum Likelihood (MLE)

77 Learning with Maximum Likelihood Log-likelihood (MLE) Maximize log-likelihood is equivalent to minimize log-likelihood (NLL)

78 Learning with Maximum Likelihood (MLE) With our normal Residual sum of squares (RSS) à We want to minimize it!

79 of MLE for Linear Regression Rewrite our as

80 of MLE for Linear Regression Rewrite our as Get the (or gradient)

81 of MLE for Linear Regression Rewrite our as Get the (or gradient) Set our to 0 Ordinary least squares solu)on

82

83 Feature weights w: Overfivng

84 A Prior on the Weight Zero-mean Gaussian prior

85 A Prior on the Weight Zero-mean Gaussian prior New

86 A Prior on the Weight Zero-mean Gaussian prior New

87 Today s Outline Basic concepts in machine learning K-nearest neighbors Linear regression Ridge regression

88 We want to minimize Ridge Regression

89 Ridge Regression We want to minimize New for the weight

90 Ridge Regression We want to minimize L2 regulariza)on New for the weight

91 Ridge Regression We want to minimize L2 regulariza)on New for the weight Leave the proof in Assignment 1!

92 What we learned Basic concepts in machine learning K-nearest neighbors Linear regression Ridge regression

93 Homework Reading Murphy ch1, ch2, and ch7 (only the covered in the lecture) Sign up at Piazza hpp://piazza.com/northeastern/spring2017/cs Start thinking about course project and find a team! Project proposal due Jan 26

94 Homework Reading Murphy ch1, ch2, and ch7 Sign up at Piazza hpp://piazza.com/northeastern/spring2017/cs Start thinking about course project and find a team! Project proposal due Jan 26 Next Time: Logis@c Regression, Decision Tree, Genera@ve Models (Naive Bayes) Reading: Murphy Ch 3, , 8.6, 16.2

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