MIT 15.071 The Analytics Edge, Spring 2017 (English) (Part 2)

   Tuesday, December 10, 2024   

Starting a new Lecture Notes Series on MIT 15.071 The Analytics Edge, Spring 2017

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Lecture 67: 4.2.7 An Introduction to Trees - Video 4: CART in R

Lecture 68: 4.2.9 An Introduction to Trees - Video 5: Random Forests

Lecture 69: 4.2.11 An Introduction to Trees - Video 6: Cross-Validation

Lecture 70: 4.2.13 An Introduction to Trees - Video 7: The Model v. The Experts

Lecture 71: 4.3.1 Healthcare Costs - Video 1: The Story of D2Hawkeye

Lecture 72: 4.3.3 Healthcare Costs - Video 2: Claims Data

Lecture 73: 4.3.5 Healthcare Costs - Video 3: The Variables

Lecture 74: 4.3.7 Healthcare Costs- Video 4: Error Measures

Lecture 75: 4.3.9 Healthcare Costs - Video 5: CART to Predict Cost

Lecture 76: 4.3.11 Healthcare Costs - Video 6: Claims Data in R

Lecture 77: 4.3.13 Healthcare Costs - Video 7: Baseline Method and Penalty Matrix

Lecture 78: 4.3.15 Healthcare Costs - Video 8: Predicting Healthcare Cost in R

Lecture 79: 4.3.17 Healthcare Costs - Video 9: Results

Lecture 80: 4.4.1 Welcome to Recitation 4 - Location, Location, Location: Regression Trees for Housing Data

Lecture 81: 4.4.2 R4. Regression Trees - Video 1: Boston Housing Data

Lecture 82: 4.4.3 R4. Regression Trees- Video 2: The Data

Lecture 83: 4.4.4 R4. Regression Trees - Video 3: Geographical Predictions

Lecture 84: 4.4.5 R4. Regression Trees - Video 4: Regression Trees

Lecture 85: 4.4.6 R4. Regression Trees - Video 5: Putting it all Together

Lecture 86: 4.4.7 R4. Regression Trees - Video 6: The CP Parameter

Lecture 87: 4.4.8 R4. Regression Trees - Video 7: Cross-Validation

Lecture 88: 5.1.1 Welcome to Unit 5 - Turning Tweets into Knowledge: An Introduction to Text Analytics

Lecture 89: 5.2.1 An Introduction to Text Analytics - Video 1: Twitter

Lecture 90: 5.2.2 An Introduction to Text Analytics - Video 2: Text Analytics

Lecture 91: 5.2.4 An Introduction to Text Analytics - Video 3: Creating the Dataset

Lecture 92: 5.2.6 An Introduction to Text Analytics - Video 4: Bag of Words

Lecture 93: 5.2.8 An Introduction to Text Analytics - Video 5: Pre-Processing in R

Lecture 94: 5.2.10 An Introduction to Text Analytics - Video 6: Bag of Words in R

Lecture 95: 5.2.12 An Introduction to Text Analytics - Video 7: Predicting Sentiment

Lecture 96: 5.2.14 An Introduction to Text Analytics - Video 8: Conclusion

Lecture 97: 5.3.1 How IBM Built a Jeopardy Champion - Video 1: IBM Watson

Lecture 98: 5.3.3 How IBM Built a Jeopardy Champion - Video 2: The Game of Jeopardy

Lecture 99: 5.3.5 How IBM Built a Jeopardy Champion - Video 3: Watson's Database and Tools

Lecture 100: 5.3.7 How IBM Built a Jeopardy Champion - Video 4: How Watson Works - Steps 1 and 2

Lecture 101: 5.3.9 How IBM Built a Jeopardy Champion - Video 5: How Watson Works - Steps 3 and 4

Lecture 102: 5.3.11 How IBM Built a Jeopardy Champion - Video 6: The Results

Lecture 103: 5.4.1 Welcome to Recitation 5 - Predictive Coding: Bringing Text Analytics to the Courtroom

Lecture 104: 5.4.2 R5. Predictive Coding - Video 1: The Story of Enron

Lecture 105: 5.4.3 R5. Predictive Coding - Video 2: The Data

Lecture 106: 5.4.4 R5. Predictive Coding - Video 3: Pre-Processing

Lecture 107: 5.4.5 R5. Predictive Coding - Video 4: Bag of Words

Lecture 108: 5.4.6 R5. Predictive Coding - Video 5: Building Models

Lecture 109: 5.4.7 R5. Predictive Coding - Video 6: Evaluating the Model

Lecture 110: 5.4.8 R5. Predictive Coding - Video 7: The ROC Curve

Lecture 111: 5.4.9 R5. Predictive Coding - Video 8: Predictive Coding Today

Lecture 112: 6.1.1 Welcome to Unit 6 - An Introduction to Clustering

Lecture 113: 6.2.1 An Introduction to Clustering - Video 1: Introduction to Netflix

Lecture 114: 6.2.3 An Introduction to Clustering - Video 2: Recommendation Systems

Lecture 115: 6.2.5 An Introduction to Clustering - Video 3: Movie Data and Clustering

Lecture 116: 6.2.7 An Introduction to Clustering - Video 4: Computing Distances

Lecture 117: 6.2.9 An Introduction to Clustering - Video 5: Hierarchical Clustering

Lecture 118: 6.2.11 An Introduction to Clustering - Video 6: Getting the Data

Lecture 119: 6.2.13 An Introduction to Clustering - Video 7: Hierarchical Clustering in R

Lecture 120: 6.2.15 An Introduction to Clustering - Video 8: The Analytics Edge of Recommendation Systems

Lecture 121: 6.3.1 Predictive Diagnosis - Video 1: Heart Attacks

Lecture 122: 6.3.3 Predictive Diagnosis - Video 2: The Data

Lecture 123: 6.3.5 Predictive Diagnosis - Video 3: Predicting Heart Attacks Using Clustering

Lecture 124: 6.3.7 Predictive Diagnosis - Video 4: Understanding Cluster Patterns

Lecture 125: 6.3.9 Predictive Diagnosis - Video 5: The Analytics Edge

Lecture 126: 6.4.1 Welcome to Recitation 6 - Seeing the Big Picture: Segmenting Images to Create Data

Lecture 127: 6.4.2 Recitation 6 - Video 1: Image Segmentation

Lecture 128: 6.4.3 R6. Segmenting Images - Video 2: Clustering Pixels

Lecture 129: 6.4.4 R6. Segmenting Images - Video 3: Hierarchical Clustering

Lecture 130: 6.4.6 R6. Segmenting Images - Video 4: MRI Image

Lecture 131: 6.4.7 R6. Segmenting Images - Video 5: K-Means Clustering

Lecture 132: 6.4.8 R6. Segmenting Images - Video 6: Detecting Tumors