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The Analytics Edge (Spring 2017)

The Analytics Edge (Spring 2017)

Prof. Dimitris Bertsimas via MIT OpenCourseWare Direct link

5.3.9 How IBM Built a Jeopardy Champion - Video 5: How Watson Works - Steps 3 and 4

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5.3.9 How IBM Built a Jeopardy Champion - Video 5: How Watson Works - Steps 3 and 4

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The Analytics Edge (Spring 2017)

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  1. 1 1.1.1 Welcome to Unit 1: An Introduction to Analytics
  2. 2 1.2.1 The Analytics Edge - Video 1: Introduction to The Analytics Edge
  3. 3 1.2.2 The Analytics Edge - Video 2: Example 1 - IBM Watson
  4. 4 1.2.3 The Analytics Edge - Video 3: Example 2 - eHarmony
  5. 5 1.2.4 The Analytics Edge - Video 4: Example 3 - The Framingham Heart Study
  6. 6 1.2.5 The Analytics Edge - Video 5: Example 4 - D2Hawkeye
  7. 7 1.2.6 The Analytics Edge - Video 6: This Class
  8. 8 1.3.2 Working with Data - Video 1: History of R
  9. 9 1.3.4 Working with Data - Video 2: Getting Started in R
  10. 10 1.3.6 Working with Data - Video 3: Vectors and Data Frames
  11. 11 1.3.8 Working with Data - Video 4: Loading Data Files
  12. 12 1.3.10 Working with Data - Video 5: Data Analysis - Summary Statistics and Scatterplots
  13. 13 1.3.12 Working with Data - Video 6: Data Analysis - Plots and Summary Tables
  14. 14 1.3.14 Working with Data - Video 7: Saving with Script Files
  15. 15 1.4.1 Welcome to Recitation 1 - Understanding Food: Nutritional Education with Data
  16. 16 1.4.2 R1. Understanding Food - Video 1: The Importance of Food and Nutrition
  17. 17 1.4.3 R1. Understanding Food - Video 2: Working with Data in R
  18. 18 1.4.4 R1. Understanding Food - Video 3: Data Analysis
  19. 19 1.4.5 R1. Understanding Food - Video 4: Creating Plots in R
  20. 20 1.4.6 R1. Understanding Food - Video 5: Adding Variables
  21. 21 1.4.7 R1. Understanding Food - Video 6: Summary Tables
  22. 22 2.1.1 Welcome to Unit 2 - An Introduction to Linear Regression
  23. 23 2.2.1 An Introduction to Linear Regression - Video 1: Predicting the Quality of Wine
  24. 24 2.2.3 An Introduction to Linear Regression - Video 2: One-variable Linear Regression
  25. 25 2.2.5 An Introduction to Linear Regression - Video 3: Multiple Linear Regression
  26. 26 2.2.7 An Introduction to Linear Regression - Video 4: Linear Regression in R
  27. 27 2.2.9 An Introduction to Linear Regression - Video 5: Understanding the Model
  28. 28 2.2.11 An Introduction to Linear Regression - Video 6: Correlation and Multicollinearity
  29. 29 2.2.13 An Introduction to Linear Regression - Video 7: Making Predictions
  30. 30 2.2.15 An Introduction to Linear Regression - Video 8: Comparing the Model to the Experts
  31. 31 2.3.2 Sports Analytics - Video 1: The Story of Moneyball
  32. 32 2.3.3 Sports Analytics - Video 2: Making It to the Playoffs
  33. 33 2.3.5 Sports Analytics - Video 3: Predicting Runs
  34. 34 2.3.7 Sports Analytics - Video 4: Using the Model to Make Predictions
  35. 35 2.3.9 Sports Analytics - Video 5: Winning the World Series
  36. 36 2.3.11 Sports Analytics - Video 6: The Analytics Edge in Sports
  37. 37 2.4.1 R2. Playing Moneyball in the NBA - Welcome to Recitation 2
  38. 38 2.4.2 R2. Moneyball in the NBA - Video 1: The Data
  39. 39 2.4.3 R2. Moneyball in the NBA - Video 2: Playoffs and Wins
  40. 40 2.4.4 R2. Moneyball in the NBA - Video 3: Points Scored
  41. 41 2.4.5 R2. Moneyball in the NBA - Video 4: Making Predictions
  42. 42 3.1.1 Welcome to Unit 3: Modeling the Expert - An Introduction to Logistical Regression
  43. 43 3.2.1 Introduction to Logistical Regression - Video 1: Replicating Expert Assessment
  44. 44 3.2.2 Introduction to Logistical Regression - Video 2: Building the Dataset
  45. 45 3.2.4 Introduction to Logistical Regression - Video 3: Logistic Regression
  46. 46 3.2.6 Introduction to Logistical Regression - Video 4: Logistic Regression in R
  47. 47 3.2.8 Introduction to Logistical Regression - Video 5: Thresholding
  48. 48 3.2.10 Introduction to Logistical Regression - Video 6: ROC Curves
  49. 49 3.2.12 Introduction to Logistical Regression - Video 7: Interpreting the Model
  50. 50 3.2.14 Introduction to Logistical Regression - Video 8: The Analytics Edge
  51. 51 3.3.1 The Framingham Heart Study - Video 1: Evaluating Risk Factors to Save Lives
  52. 52 3.3.3 The Framingham Heart Study - Video 2: Risk Factors
  53. 53 3.3.5 The Framingham Heart Study - Video 3: A Logistical Regression Model
  54. 54 3.3.7 The Framingham Heart Study - Video 4: Validating the Model
  55. 55 3.3.9 The Framingham Heart Study - Video 5: Interventions
  56. 56 3.3.11 The Framingham Heart Study - Video 6: Overall Impact
  57. 57 3.4.1 Recitation 3 - Election Forecasting: Predicting the Winner Before Any Votes Are Cast
  58. 58 3.4.2 R3. Election Forecasting - Video 1: Election Prediction
  59. 59 3.4.3 R3. Election Forecasting - Video 2: Dealing with Missing Data
  60. 60 3.4.4 R3. Election Forecasting - Video 3: A Sophisticated Baseline Method
  61. 61 3.4.5 R3. Election Forecasting - Video 4: Logistic Regression Models
  62. 62 3.4.6 R3. Election Forecasting - Video 5: Test Set Predictions
  63. 63 4.1.1 Welcome to Unit 4 - Judge, Jury, and Classifier: An Introduction to Trees
  64. 64 4.2.1 An Introduction to Trees - Video 1: The Supreme Court
  65. 65 4.2.3 An Introduction to Trees - Video 2: CART
  66. 66 4.2.5 An Introduction to Trees - Video 3: Splitting and Predictions
  67. 67 4.2.7 An Introduction to Trees - Video 4: CART in R
  68. 68 4.2.9 An Introduction to Trees - Video 5: Random Forests
  69. 69 4.2.11 An Introduction to Trees - Video 6: Cross-Validation
  70. 70 4.2.13 An Introduction to Trees - Video 7: The Model v. The Experts
  71. 71 4.3.1 Healthcare Costs - Video 1: The Story of D2Hawkeye
  72. 72 4.3.3 Healthcare Costs - Video 2: Claims Data
  73. 73 4.3.5 Healthcare Costs - Video 3: The Variables
  74. 74 4.3.7 Healthcare Costs- Video 4: Error Measures
  75. 75 4.3.9 Healthcare Costs - Video 5: CART to Predict Cost
  76. 76 4.3.11 Healthcare Costs - Video 6: Claims Data in R
  77. 77 4.3.13 Healthcare Costs - Video 7: Baseline Method and Penalty Matrix
  78. 78 4.3.15 Healthcare Costs - Video 8: Predicting Healthcare Cost in R
  79. 79 4.3.17 Healthcare Costs - Video 9: Results
  80. 80 4.4.1 Welcome to Recitation 4 - Location, Location, Location: Regression Trees for Housing Data
  81. 81 4.4.2 R4. Regression Trees - Video 1: Boston Housing Data
  82. 82 4.4.3 R4. Regression Trees- Video 2: The Data
  83. 83 4.4.4 R4. Regression Trees - Video 3: Geographical Predictions
  84. 84 4.4.5 R4. Regression Trees - Video 4: Regression Trees
  85. 85 4.4.6 R4. Regression Trees - Video 5: Putting it all Together
  86. 86 4.4.7 R4. Regression Trees - Video 6: The CP Parameter
  87. 87 4.4.8 R4. Regression Trees - Video 7: Cross-Validation
  88. 88 5.1.1 Welcome to Unit 5 - Turning Tweets into Knowledge: An Introduction to Text Analytics
  89. 89 5.2.1 An Introduction to Text Analytics - Video 1: Twitter
  90. 90 5.2.2 An Introduction to Text Analytics - Video 2: Text Analytics
  91. 91 5.2.4 An Introduction to Text Analytics - Video 3: Creating the Dataset
  92. 92 5.2.6 An Introduction to Text Analytics - Video 4: Bag of Words
  93. 93 5.2.8 An Introduction to Text Analytics - Video 5: Pre-Processing in R
  94. 94 5.2.10 An Introduction to Text Analytics - Video 6: Bag of Words in R
  95. 95 5.2.12 An Introduction to Text Analytics - Video 7: Predicting Sentiment
  96. 96 5.2.14 An Introduction to Text Analytics - Video 8: Conclusion
  97. 97 5.3.1 How IBM Built a Jeopardy Champion - Video 1: IBM Watson
  98. 98 5.3.3 How IBM Built a Jeopardy Champion - Video 2: The Game of Jeopardy
  99. 99 5.3.5 How IBM Built a Jeopardy Champion - Video 3: Watson's Database and Tools
  100. 100 5.3.7 How IBM Built a Jeopardy Champion - Video 4: How Watson Works - Steps 1 and 2
  101. 101 5.3.9 How IBM Built a Jeopardy Champion - Video 5: How Watson Works - Steps 3 and 4
  102. 102 5.3.11 How IBM Built a Jeopardy Champion - Video 6: The Results
  103. 103 5.4.1 Welcome to Recitation 5 - Predictive Coding: Bringing Text Analytics to the Courtroom
  104. 104 5.4.2 R5. Predictive Coding - Video 1: The Story of Enron
  105. 105 5.4.3 R5. Predictive Coding - Video 2: The Data
  106. 106 5.4.4 R5. Predictive Coding - Video 3: Pre-Processing
  107. 107 5.4.5 R5. Predictive Coding - Video 4: Bag of Words
  108. 108 5.4.6 R5. Predictive Coding - Video 5: Building Models
  109. 109 5.4.7 R5. Predictive Coding - Video 6: Evaluating the Model
  110. 110 5.4.8 R5. Predictive Coding - Video 7: The ROC Curve
  111. 111 5.4.9 R5. Predictive Coding - Video 8: Predictive Coding Today
  112. 112 6.1.1 Welcome to Unit 6 - An Introduction to Clustering
  113. 113 6.2.1 An Introduction to Clustering - Video 1: Introduction to Netflix
  114. 114 6.2.3 An Introduction to Clustering - Video 2: Recommendation Systems
  115. 115 6.2.5 An Introduction to Clustering - Video 3: Movie Data and Clustering
  116. 116 6.2.7 An Introduction to Clustering - Video 4: Computing Distances
  117. 117 6.2.9 An Introduction to Clustering - Video 5: Hierarchical Clustering
  118. 118 6.2.11 An Introduction to Clustering - Video 6: Getting the Data
  119. 119 6.2.13 An Introduction to Clustering - Video 7: Hierarchical Clustering in R
  120. 120 6.2.15 An Introduction to Clustering - Video 8: The Analytics Edge of Recommendation Systems
  121. 121 6.3.1 Predictive Diagnosis - Video 1: Heart Attacks
  122. 122 6.3.3 Predictive Diagnosis - Video 2: The Data
  123. 123 6.3.5 Predictive Diagnosis - Video 3: Predicting Heart Attacks Using Clustering
  124. 124 6.3.7 Predictive Diagnosis - Video 4: Understanding Cluster Patterns
  125. 125 6.3.9 Predictive Diagnosis - Video 5: The Analytics Edge
  126. 126 6.4.1 Welcome to Recitation 6 - Seeing the Big Picture: Segmenting Images to Create Data
  127. 127 6.4.2 Recitation 6 - Video 1: Image Segmentation
  128. 128 6.4.3 R6. Segmenting Images - Video 2: Clustering Pixels
  129. 129 6.4.4 R6. Segmenting Images - Video 3: Hierarchical Clustering
  130. 130 6.4.6 R6. Segmenting Images - Video 4: MRI Image
  131. 131 6.4.7 R6. Segmenting Images - Video 5: K-Means Clustering
  132. 132 6.4.8 R6. Segmenting Images - Video 6: Detecting Tumors
  133. 133 6.4.9 R6. Segmenting Images - Video 7: Comparing Methods
  134. 134 7.1.1 Welcome to Unit 7 - Visualizing the World: An Introduction to Visualization
  135. 135 7.2.1 An Introduction to Visualization - Video 1: The Power of Visualizations
  136. 136 7.2.3 An Introduction to Visualization - Video 2: The World Health Organization (WHO)
  137. 137 7.2.5 An Introduction to Visualization - Video 3: What is Data Visualization?
  138. 138 7.2.7 An Introduction to Visualization - Video 4: Basic Scatterplots Using ggplot
  139. 139 7.2.9 An Introduction to Visualization - Video 5: Advanced Scatterplots Using ggplot
  140. 140 7.3.1 Visualization for Law and Order - Video 1: Predictive Policing
  141. 141 7.3.3 Visualization for Law and Order - Video 2: Visualizing Crime Over Time
  142. 142 7.3.5 Visualization for Law and Order - Video 3: A Line Plot
  143. 143 7.3.7 Visualization for Law and Order - Video 4: A Heatmap
  144. 144 7.3.9 Visualization for Law and Order - Video 5: A Geographical Hot Spot Map
  145. 145 7.3.11 Visualization for Law and Order - Video 6: A Heatmap on the United States
  146. 146 7.3.13 Visualization for Law and Order - Video 7: The Analytics Edge
  147. 147 7.4.1 Welcome to Recitation 7 - The Good, the Bad, and the Ugly in Visualization
  148. 148 7.4.2 R7. Visualization - Video 1: Introduction
  149. 149 7.4.3 R7. Visualization - Video 2: Pie Charts
  150. 150 7.4.4 R7. Visualization - Video 3: Bar Charts in R
  151. 151 7.4.5 R7. Visualization - Video 4: A Better Visualization
  152. 152 7.4.6 R7. Visualization - Video 5: World Maps in R
  153. 153 7.4.7 R7. Visualization - Video 6: Scales
  154. 154 7.4.8 R7. Visualization - Video 7: Using Line Charts Instead
  155. 155 8.1.1 Welcome to Unit 8 - Airline Revenue Management: An Introduction to Linear Optimization
  156. 156 8.2.1 An Introduction to Linear Optimization - Video 1: Introduction
  157. 157 8.2.2 An Introduction to Linear Optimization - Video 2: A Single Flight
  158. 158 8.2.4 An Introduction to Linear Optimization - Video 3: The Problem Formulation
  159. 159 8.2.6 An Introduction to Linear Optimization - Video 4: Solving the Problem
  160. 160 8.2.8 An Introduction to Linear Optimization - Video 5: Visualizing the Problem
  161. 161 8.2.10 An Introduction to Linear Optimization - Video 6: Sensitivity Analysis
  162. 162 8.2.12 An Introduction to Linear Optimization - Video 7: Connecting Flights
  163. 163 8.2.14 An Introduction to Linear Optimization - Video 8: The Edge of Revenue Management
  164. 164 8.3.1 An Application of Linear Optimization - Video 1: Introduction to Radiation Therapy
  165. 165 8.3.3 Radiation Therapy - Video 2: An Optimization Problem
  166. 166 8.3.5 Radiation Therapy - Video 3: Solving the Problem
  167. 167 8.3.7 Radiation Therapy - Video 4: A Head and Neck Case
  168. 168 8.3.9 Radiation Therapy - Video 5: Sensitivity Analysis
  169. 169 8.3.11 Radiation Therapy - Video 6: The Analytics Edge
  170. 170 8.4.1 Welcome to Recitation 8 - Google AdWords: Optimizing Online Advertising
  171. 171 8.4.2 R8. Google AdWords - Video 1: Introduction
  172. 172 8.4.3 R8. Google AdWords - Video 2: How Online Advertising Works
  173. 173 8.4.4 R8. Google AdWords - Video 3: Prices and Queries
  174. 174 8.4.5 R8. Google AdWords - Video 4: Modeling the Problem
  175. 175 8.4.6 R8. Google AdWords - Video 5: Solving the Problem
  176. 176 8.4.7 R8. Google AdWords - Video 6: A Greedy Approach
  177. 177 8.4.8 R8. Google AdWords - Video 7: Sensitivity Analysis
  178. 178 8.4.9 R8. Google AdWords - Video 8: Extensions and the Edge
  179. 179 9.1.1 Welcome to Unit 9: An Introduction to Integer Optimization
  180. 180 9.2.1 Sports Scheduling - Video 1: Introduction
  181. 181 9.2.3 Sports Scheduling - Video 2: The Optimization Problem
  182. 182 9.2.5 Sports Scheduling - Video 3: Solving the Problem
  183. 183 9.2.7 Sports Scheduling - Video 4: Logical Constraints
  184. 184 9.2.9 Sports Scheduling - Video 5: The Edge
  185. 185 9.3.1 eHarmony - Video 1: The Goal of eHarmony
  186. 186 9.3.3 eHarmony - Video 2: Using Integer Optimization
  187. 187 9.3.5 eHarmony - Video 3: Predicting Compatibility Scores
  188. 188 9.3.7 eHarmony - Video 4: The Analytics Edge
  189. 189 9.4.1 Welcome to Recitation 9 - Operating Room Scheduling: Making Hospitals Run Smoothly
  190. 190 9.4.2 R9. Operating Room Scheduling - Video 1: The Problem
  191. 191 9.4.3 R9. Operating Room Scheduling - Video 2: An Optimization Model
  192. 192 9.4.4 R9. Operating Room Scheduling - Video 3: Solving the Problem
  193. 193 9.4.5 R9. Operating Room Scheduling - Video 4: The Solution

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