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Est ISYE 6501 Lecture Transcripts About This Document This document was originally created in the summer of 2017 and is maintained collaboratively through the efforts of the students of edX GTx ISY ... E 6501 using transcripts and screenshots from the video lectures. You are strongly encouraged to improve the formatting, layout, add or adjust images, bold key words, and even condense copy and remove personal asides (sorry, Professor Sokol). It is expected that sections may be added, removed, or modified -- in which case, again, you should please take the liberty of adjusting this document to match. Some tips on formatting: the Weeks (e.g., Week 1), Modules (e.g., 1. Introduction), and Sub-Modules (e.g., 1.3 (C): What is Modeling?) are formatted respectively as Heading 1, Heading 2, and Heading 3. These can be adjusted in the format menu, or by pressing Ctrl + 1, Ctrl + 2, and Ctrl + 3, respectively, while the cursor is on the line you wish to change. Pressing Ctrl + , will toggle subscript text on and off. For use as study material for GTx 6501x. Text and images copied directly from the course videos, with light editing for readability. Not intended for broad distribution or general use. Content is the intellectual property of Joel Sokol and Georgia Tech. 1ISYE 6501 Lecture Transcripts 0 About This Document 0 Week 1 6 1. Introduction 6 1.1 (C): Introduction to Analytics Modeling 6 1.2 (C): Introduction to the Course 9 1.3 (C): What is Modeling? 11 2. Classification 12 2.1 (M): Introduction to Classification 12 2.2 (M): Choosing a Classifier 14 2.3 (C): Data Definitions 19 2.4 (M): Support Vector Machines (SVM) 21 2.5 (M): SVM: What the Name Means 25 2.6 (M): Advanced SVM 27 2.7 (C): Scaling and Standardization 30 2.8 (M): K-Nearest Neighbor Classification 33 Week 2 35 3. Validation 35 3.1 (C): Introduction to Validation 35 3.2 (C): Validation and Test Data Sets 37 3.3 (C): Splitting Data 39 3.4 (C): Cross-Validation 41 4. Clustering 43 4.1 (M): Introduction to Clustering 43 Examples of Clustering 43 [IMAGE] 44 [IMAGE] 44 4.2 (C): Distance Norms 45 4.3 (M): K-means Clustering 48 4.4 (M): Practical Details for K-Means 50 4.5 (M): Clustering for Prediction 52 4.6 (M): Supervised vs Unsupervised Learning 54 2Week 3 55 5. Basic Data Preparation 55 5.1 (C): Introduction to Data Preparation 55 5.2 (C): Outlier Detection 56 [IMAGE] 56 [IMAGE] 56 [IMAGE] 57 [IMAGE] 57 [IMAGE] 57 5.3 (C): Dealing with Outliers 58 6. Change Detection 60 6.1 (M): Introduction to Change Detection 60 Case Study: Semiconductor Manufacturing Traffic 60 Case Study: Railroad Safety 61 [IMAGE] 61 [IMAGE] 61 6.2 (M): CUSUM for Change Detection 62 [IMAGE] 63 [IMAGE] 63 [IMAGE] 63 [IMAGE] 64 6.3 (M): Change-Detection Homework Follow-up 65 Week 4 67 7. Time Series Models 67 7.1 (M): Introduction to Exponential Smoothing 67 7.2 (M): Trends and Cyclic Effects 71 [FORMULA SCREENCAP] 71 7.3 (M): Exponential Smoothing: What The Name Means 76 [IMAGE] 76 7.4 (M): Forecasting 78 7.5 (M): ARIMA 80 7.6 (M): GARCH 85 [IMAGE] 85 3Week 5 87 8. Basic Regression 87 8.1 (M): Introduction to Regression 87 [IMAGE] 87 [IMAGE] 88 8.2 (C): Maximum Likelihood and Information Criteria 90 8.3 (M): Using Regression 93 8.4 (C): Causation vs Correlation 95 8.5 (M): Transformation and Interactions 98 8.6 (M): Regression Output 99 Week 6 101 9. Advanced Data Preparation 101 9.1 (C): Box-Cox Transformation 101 9.2 (C): De-Trending 103 9.3 (C): Introduction to Principal Component Analysis 105 9.4 (C): Using Principal Component Analysis 107 9.5 (C): Eigenvalues and Eigenvectors 109 Week 7 109 10. Advanced Regression 110 10.1 (M): Introduction to CART 110 10.2: (M): Branching 112 10.3: (M): Random Forests 114 10.4: (M): Logistic Regression 116 10.5: (C): Confusion Matrices 119 10.6: (C): Situationally-Driven Comparison 122 10.7: (M): Advanced Topics in Regression 124 Week 8 126 11. Variable Selection 126 11.1: (C): Introduction to Variable Selection 126 11.2: (C): Models for Variable Selection 129 11.3: (C): Choosing a Variable Selection Model 133 12. Design of Experiments 136 12.1 (M): Introduction to Design of Experiments 136 412.2 (M): A/B Testing 138 Case Study: Banner Ad Test 138 12.3 (M): Factorial Designs 140 12.4 (M): Multi-Armed Bandits 143 13. Probability-Based Models 145 13.1 (M): Introduction to Advanced Probability Distributions 145 Case Study: Baseball Ticket Upgrades 145 13.2 (M): Bernoulli, Binomial and Geometric Distributions 147 13.3 (M): Poisson, Exponential and Weibull Distributions 151 Case Study: Atlanta Airport Arrivals 152 13.4 (C): Q-Q Plots 155 13. Probability-Based Models, Continued 158 13.5 (M): Queuing 158 13.6 (M): Simulation Basics 161 13.7 (M): Prescriptive Simulation 164 13.8 (M): Markov Chains 166 14. Missing Data 168 14.1 (C): Introduction to Missing Data 168 14.2 (C): Methods That Do Not Require Imputation 171 Method 1. Throw Away Data 171 Method 2. Use Categorical Variables 171 14.3 (C): Imputation Methods 173 Imputation Method: Use An Average 173 Imputation Method: Use A Predictive Model 173 Imputation Method: Perturbation 174 15. Optimization 176 15.1 (M): Introduction to Optimization 176 15.2 (M): Elements of Optimization Models 178 15.3 (M): Modeling is an Art: Two Examples 180 Case Study: The Army Diet Problem 180 15.4 (M): Modeling With Binary Variables 183 Week 9 184 15.5 (M): Optimization for Statistical Models 186 15.6 (M): Classification of Optimization Models 189 515.7 (M): Stochastic Optimization 192 15.8 (M): Basic Optimization Algorithms 195 16.1 (M): Non-Parametric Methods 197 16.2 (M): Bayesian Modeling 200 16.3 (M): Communities in Graphs 204 16.4 (M): Neural Networks and Deep Learning 207 16.5 (M): Competitive Models 209 16.5a (M): Competitive Models Demo 212 Week 10 215 17.1 A Format For Discussion 216 18.1 Introduction to Power Company Case 217 18.2 Models for Customer Identification 218 18.3 (X): Models for Cost Estimation 220 18.4 (X): Models for Shutoff Selection 222 Week 11 222 19.1 (X): Introduction to Retailer Case 22 [Show More]

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