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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