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Introduction to Machine Learning

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Andrew Ferlitsch

Abstract: This PDSG workshop introduces basic concepts on machine learning. The course covers fundamentals of Supervised and Unsupervised Learning, Decision Trees, Pruning, Ensemble Trees, Linear Regressions, Loss Functions, K-means, and dataset preparation. Level: Fundamental Requirements: No prior programming or statistics knowledge required.

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Introduction to Machine Learning Portland Data Science Group Created by Andrew Ferlitsch Community Outreach Officer May, 2017
ML : DS vs CS MLDATA SCIENCE COMPUTER SCIENCE Machine Learning is a marriage of Statistics and Computer Science Traditional Front Ends
Categories ML Regression/Classification (Prediction) Reinforcement Learning (Robotics) Neural Networks (Recognition) Natural Language Processing (Speech/Language)
Solutions ML Statistics Computer Science Classification Prediction Forecasting Recommendation Association Apple vs Pear Life Expectancy Weather Shelf Placement Suggestions Computer Vision Speech Recognition Language Dynamic Adaption Road Signs Voice to Text Translation Audio RecognitionAutomation Gun Shots Examples
Ladder ( Complexity == Salary) Marketing / Sales Text Classification Human Interface Computer Vision Factory Automation 3D Printing Autonomous Space Frontier Emergent Mature Growth Very Good $$ Exceptional $$ Stratosphere $$
It’s About Training Machine Learning is about using data to train a model DATA Training Data Test Data Train Split Dataset into Training and Test Model Use Training Data to Train the Model Produce Model Test the Model Accuracy Determine Accuracy
It’s in the Label Features + Label (where label is what the item [row] is. e.g., apple. Supervised versus Unsupervised Learning Labeled Data Unlabeled Data Features Only. e.g., we do not know its an apple • Human (or program) pre-label the data. • Learn how features map to labels. • Learn how features map to clusters. • Learn how clusters map to labels.
Supervised Learning Feature 1 Feature 2 Feature 3 Feature 4 Label real-value real-value real-value categorical-value category/value real-value real-value real-value categorical-value category/value real-value real-value real-value categorical-value category/value Attributes of each sample What the sample is Weight (oz) Width (in) Height (in) Color Label 6 3 3.5 green apple 2 1.5 8 yellow banana 7 4 4.7 yellow apple Example Little SignificanceGreater Significance
Decision Tree Simple Training Model 1st Feature 2nd Feature 2nd Feature green yellow 3rd Feature 3rd Feature 3rd Feature 3rd Feature color weight < 4 >= 4 < 3.5 >= 3.5 width height e.g., yellow apples weigh more then green apples 4th Feature 4th Feature 4th Feature banana apple banana apple Learn thresholds Leaves are the classification apple
Pruning Weight (oz) Width (in) Height (in) Color Label 6 3 3.5 green apple 2 1.5 8 yellow banana 7 4 4.7 yellow apple Assume does not contribute to outcome Weight (oz) Width (in) Height (in) Label 6 3 3.5 apple 2 1.5 8 banana 7 4 4.7 apple
Decision Tree After Pruning Simple Training Model 1st Feature 2nd Feature 2nd Feature < 4 >= 4 3rd Feature 3rd Feature 3rd Feature 3rd Feature weight width < 2 >= 2 < 2.5 >= 2.5 height banana apple banana apple Leaves are the classification apple > 3 <= 3
Ensemble – Decision Stumps Decision Stumps – Weak Learners 1st Feature 2nd Feature < 4 >= 4 3rd Feature weight width < 2.5 >= 2.5 height banana apple banana apple apple <= 4> 4 banana MAJORITY VOTE Weight: 4.2 = Apple Width : 2.3 = Banana Height : 5.5 = Banana VOTE = Banana
(Simple) Linear Regression It’s In The Line Age (x) 0 Feature (data) Spend (y) Label (learn) Data Plotted (Scatter) Best Fitted Line y = a + bx a bx (slope)
Loss Function Minimize Loss (Estimated Error) when Fitting a Line y1 Actual Values (y) Predicted Values (yhat) y2 y3 y4 y5 y6 1 𝑛 𝑗=1 𝑛 (𝑦 − 𝑦ℎ𝑎𝑡)2 MSE = (y – yhat) Mean Square Error Sum the Square of the Difference Divide by the number of samples
Libraries Do the Work Python & R have libraries that do the math! e.g., numPy, sci-learn Split the Dataset X_train, X_test = split( dataset, 0.80 ) Train the Model dataset percentage (e.g., 80% train, 20% test)training & test data model = train( X_train, 4 ) training datamethod trained model Test the Model Y_test = model( X_test, 4 ) column of label predicted values trained model test data column of label Calculate Accuracy result = accuracy( X_test, 4, Y_test ) actual value predicted values Pseudo Names
Unsupervised Learning Feature 1 Feature 2 Feature 3 Feature 4 real-value real-value real-value categorical-value real-value real-value real-value categorical-value real-value real-value real-value categorical-value Attributes of each sample Weight (oz) Width (in) Height (in) Color 6 3 3.5 green 2 1.5 8 yellow 7 4 4.7 yellow Example There is NO label – we don’t know what each sample is in the training set!
Clusters It’s In The Cluster Height (x1) 0 Weight (x2) Data Plotted (Scatter) Cluster (e.g., Apple) Cluster (e.g., Banana) Find a Relationship Between Data that Separates them into Clusters
K-Means Height (x1)0 Weight (x2) Cluster Centroid • Pick Number of Clusters (e.g., 2 for Apple and Banana) • Place a point (cluster centroid) randomly for each cluster • Assign each sample to a cluster based on closest cluster centroid Calculate Distance to each centroid
Recalculate Centroids Height (x1)0 Weight (x2) Previous Cluster Centroid • Calculate Centroid (Center) of each Cluster • Move Centroid to new calculated location. • Assign each sample to a cluster based on closest (new) cluster centroid New Centroids Recalculate Distances REPEAT STEPS until centroids do not move anymore
Preparing a Dataset - Clean Clean • Fix/Remove unreadable entries • e.g., bad (funny) characters from different character codesets • Fix/Remove Misaligned entries • e.g., incorrect number of fields for row in a CSV file. • Replace blank fields (i.e., synthesize a value). • e.g., Mean value of all non-blank values • e.g., Use rows with values as training set to learn the value Dataset
Preparing a Dataset – Conversion Categorical Value Conversion • Change categorical values into real values • Cannot use enumeration (values imply importance!) • Expand into dummy variables, one per category, use 0 and 1 as values CleanDataset Fruit Apple Banana Pear Apple Banana Pear 1 1 1
Preparing a Dataset – Feature Scaling Feature Scaling • Scale values to be within the same proportional range • A column with much larger range will over influence learning over another column with smaller range. • Typically, scale the range between 0 and 1 (normalization) or -1 and 1 (standardization). Categorical Value Conversion CleanDataset X’ = 𝑥 − min(𝑥) max 𝑥 − min(𝑥) Normalization original valuenew value

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Introduction to Machine Learning

  • 1. Introduction to Machine Learning Portland Data Science Group Created by Andrew Ferlitsch Community Outreach Officer May, 2017
  • 2. ML : DS vs CS MLDATA SCIENCE COMPUTER SCIENCE Machine Learning is a marriage of Statistics and Computer Science Traditional Front Ends
  • 4. Solutions ML Statistics Computer Science Classification Prediction Forecasting Recommendation Association Apple vs Pear Life Expectancy Weather Shelf Placement Suggestions Computer Vision Speech Recognition Language Dynamic Adaption Road Signs Voice to Text Translation Audio RecognitionAutomation Gun Shots Examples
  • 5. Ladder ( Complexity == Salary) Marketing / Sales Text Classification Human Interface Computer Vision Factory Automation 3D Printing Autonomous Space Frontier Emergent Mature Growth Very Good $$ Exceptional $$ Stratosphere $$
  • 6. It’s About Training Machine Learning is about using data to train a model DATA Training Data Test Data Train Split Dataset into Training and Test Model Use Training Data to Train the Model Produce Model Test the Model Accuracy Determine Accuracy
  • 7. It’s in the Label Features + Label (where label is what the item [row] is. e.g., apple. Supervised versus Unsupervised Learning Labeled Data Unlabeled Data Features Only. e.g., we do not know its an apple • Human (or program) pre-label the data. • Learn how features map to labels. • Learn how features map to clusters. • Learn how clusters map to labels.
  • 8. Supervised Learning Feature 1 Feature 2 Feature 3 Feature 4 Label real-value real-value real-value categorical-value category/value real-value real-value real-value categorical-value category/value real-value real-value real-value categorical-value category/value Attributes of each sample What the sample is Weight (oz) Width (in) Height (in) Color Label 6 3 3.5 green apple 2 1.5 8 yellow banana 7 4 4.7 yellow apple Example Little SignificanceGreater Significance
  • 9. Decision Tree Simple Training Model 1st Feature 2nd Feature 2nd Feature green yellow 3rd Feature 3rd Feature 3rd Feature 3rd Feature color weight < 4 >= 4 < 3.5 >= 3.5 width height e.g., yellow apples weigh more then green apples 4th Feature 4th Feature 4th Feature banana apple banana apple Learn thresholds Leaves are the classification apple
  • 10. Pruning Weight (oz) Width (in) Height (in) Color Label 6 3 3.5 green apple 2 1.5 8 yellow banana 7 4 4.7 yellow apple Assume does not contribute to outcome Weight (oz) Width (in) Height (in) Label 6 3 3.5 apple 2 1.5 8 banana 7 4 4.7 apple
  • 11. Decision Tree After Pruning Simple Training Model 1st Feature 2nd Feature 2nd Feature < 4 >= 4 3rd Feature 3rd Feature 3rd Feature 3rd Feature weight width < 2 >= 2 < 2.5 >= 2.5 height banana apple banana apple Leaves are the classification apple > 3 <= 3
  • 12. Ensemble – Decision Stumps Decision Stumps – Weak Learners 1st Feature 2nd Feature < 4 >= 4 3rd Feature weight width < 2.5 >= 2.5 height banana apple banana apple apple <= 4> 4 banana MAJORITY VOTE Weight: 4.2 = Apple Width : 2.3 = Banana Height : 5.5 = Banana VOTE = Banana
  • 13. (Simple) Linear Regression It’s In The Line Age (x) 0 Feature (data) Spend (y) Label (learn) Data Plotted (Scatter) Best Fitted Line y = a + bx a bx (slope)
  • 14. Loss Function Minimize Loss (Estimated Error) when Fitting a Line y1 Actual Values (y) Predicted Values (yhat) y2 y3 y4 y5 y6 1 𝑛 𝑗=1 𝑛 (𝑦 − 𝑦ℎ𝑎𝑡)2 MSE = (y – yhat) Mean Square Error Sum the Square of the Difference Divide by the number of samples
  • 15. Libraries Do the Work Python & R have libraries that do the math! e.g., numPy, sci-learn Split the Dataset X_train, X_test = split( dataset, 0.80 ) Train the Model dataset percentage (e.g., 80% train, 20% test)training & test data model = train( X_train, 4 ) training datamethod trained model Test the Model Y_test = model( X_test, 4 ) column of label predicted values trained model test data column of label Calculate Accuracy result = accuracy( X_test, 4, Y_test ) actual value predicted values Pseudo Names
  • 16. Unsupervised Learning Feature 1 Feature 2 Feature 3 Feature 4 real-value real-value real-value categorical-value real-value real-value real-value categorical-value real-value real-value real-value categorical-value Attributes of each sample Weight (oz) Width (in) Height (in) Color 6 3 3.5 green 2 1.5 8 yellow 7 4 4.7 yellow Example There is NO label – we don’t know what each sample is in the training set!
  • 17. Clusters It’s In The Cluster Height (x1) 0 Weight (x2) Data Plotted (Scatter) Cluster (e.g., Apple) Cluster (e.g., Banana) Find a Relationship Between Data that Separates them into Clusters
  • 18. K-Means Height (x1)0 Weight (x2) Cluster Centroid • Pick Number of Clusters (e.g., 2 for Apple and Banana) • Place a point (cluster centroid) randomly for each cluster • Assign each sample to a cluster based on closest cluster centroid Calculate Distance to each centroid
  • 19. Recalculate Centroids Height (x1)0 Weight (x2) Previous Cluster Centroid • Calculate Centroid (Center) of each Cluster • Move Centroid to new calculated location. • Assign each sample to a cluster based on closest (new) cluster centroid New Centroids Recalculate Distances REPEAT STEPS until centroids do not move anymore
  • 20. Preparing a Dataset - Clean Clean • Fix/Remove unreadable entries • e.g., bad (funny) characters from different character codesets • Fix/Remove Misaligned entries • e.g., incorrect number of fields for row in a CSV file. • Replace blank fields (i.e., synthesize a value). • e.g., Mean value of all non-blank values • e.g., Use rows with values as training set to learn the value Dataset
  • 21. Preparing a Dataset – Conversion Categorical Value Conversion • Change categorical values into real values • Cannot use enumeration (values imply importance!) • Expand into dummy variables, one per category, use 0 and 1 as values CleanDataset Fruit Apple Banana Pear Apple Banana Pear 1 1 1
  • 22. Preparing a Dataset – Feature Scaling Feature Scaling • Scale values to be within the same proportional range • A column with much larger range will over influence learning over another column with smaller range. • Typically, scale the range between 0 and 1 (normalization) or -1 and 1 (standardization). Categorical Value Conversion CleanDataset X’ = 𝑥 − min(𝑥) max 𝑥 − min(𝑥) Normalization original valuenew value