Machine Learning Introduction
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This document provides an introduction to machine learning concepts including linear regression, neural networks, and model evaluation. It discusses using machine learning for tasks like image recognition, text translation, and medical applications. Linear regression is demonstrated for predicting housing prices based on area. Neural networks are explained for classification problems with examples of predicting a developer type based on experience. The document outlines model training, calculating costs, tuning hyperparameters, and evaluating models on test vs. cross-validation data to avoid overfitting. Caveats are noted about omitted math details.
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Machine Learning Introduction
- 2. Applied Areas • Image Recognition, Voice Recognition • Text Translation • Ad Tech • Medical • Biology • Industry
- 4. Prediction of Housing Price area (m2) price (USD) How much is it when the area is 50 m2 ? 50 ?
- 5. Collect Data with Label area (m2) price (USD) Label: Output what we want to know. Feature(s): Input(s) to predict output.
- 6. Cost area (m2) price (USD) Cost Model: y = θ0 + θ1 * x
- 7. Cost Function: J(θ1, θ2) Find θ1 and θ2 which minimizes J(θ1, θ2). How to get a model
- 8. Predict area (m2) price (USD) Model: y = θ0 + θ1 * x How much is it when the area is 50 m2 ? 50 100000
- 10. Categorical Classification Java 7 years 0 years 1 years 3 years Ruby 0 years 0 years 2 years 2 years ObjC 2 years 5 years 0 years 0 years PHP 0 years 2 years 5 years 1 years Android Developer iOS Developer Web Developer Label Features ? Experience
- 11. Binary Classification Male(1) Male(1) Female(0) Java 7 years 0 years 1 years 3 years Ruby 0 years 0 years 2 years 2 years ObjC 2 years 5 years 0 years 0 years PHP 0 years 2 years 5 years 1 years ?
- 15. Neural Network x1 x2 x3 x4 y1 y2 y3 Java Ruby ObjC PHP Android iOS Web θ11 θ12 θ13 θ21 θ22 θ23 θ31 θ32 θ33 θ41 θ42 θ43 y1 = θ11 * x1 + θ21 * x2 + θ31 * x3 + θ41 * x4 y2 = θ12 * x1 + θ22 * x2 + θ32 * x3 + θ42 * x4 y3 = θ13 * x1 + θ23 * x2 + θ33 * x3 + θ43 * x4 I omitted bias term.
- 16. Neural Network x1 x2 x3 x4 y1 y2 y3 Java Ruby ObjC PHP Android iOS Web I omitted bias term. θ11 θ12 θ13
- 17. Neural Network y1 = θ11 * x1 + θ21 * x2 + θ31 * x3 + θ41 * x4 y2 = θ12 * x1 + θ22 * x2 + θ32 * x3 + θ42 * x4 y3 = θ13 * x1 + θ23 * x2 + θ33 * x3 + θ43 * x4 I omitted bias term. y1 = θ11 * x1 + θ21 * x2 + θ31 * x3 + θ41 * x4 y2 = θ12 * x1 + θ22 * x2 + θ32 * x3 + θ42 * x4 y3 = θ13 * x1 + θ23 * x2 + θ33 * x3 + θ43 * x4 y1 = θ11 * x1 + θ21 * x2 + θ31 * x3 + θ41 * x4 y2 = θ12 * x1 + θ22 * x2 + θ32 * x3 + θ42 * x4 y3 = θ13 * x1 + θ23 * x2 + θ33 * x3 + θ43 * x4 m=1 m=2 m=3
- 18. Neural Network XΘ = Y θ11 θ12 θ13 θ21 θ22 θ23 θ31 θ32 θ33 θ41 θ42 θ43 x11 x12 x13 x14 x21 x22 x23 x24 x31 x32 x33 x34 y11 y12 y13 y21 y22 y23 y31 y32 y33 3 x 4 (m x n) 4 x 3 3 x 3 θ11 θ12 θ13 θ21 θ22 θ23 θ31 θ32 θ33 θ41 θ42 θ43 7 0 2 0 0 0 5 2 1 2 0 5 1 0 0 0 1 0 0 0 1
- 19. Calculate an example of Y 7 0 2 0 0 0 5 2 1 2 0 5 1 4 2 3 2 4 2 3 1 3 1 2 11 34 16 16 17 9 22 13 20 Example of Theta: Θex 0.18 0.55 0.26 0.38 0.40 0.21 0.40 0.23 0.36 Output: Yex
- 20. Cost 0.18 0.55 0.26 0.38 0.40 0.21 0.40 0.23 0.36 Yex 1 0 0 0 1 0 0 0 1 Y Cost : J(Θex) = (1-0.18)2 + (0.55-0)2 + (0.26-0)2 + (0.38-0)2 + (0.40-1)2 + (0.21-0)2 + (0.40-0)2 + (0.23-1)2 + (0.36-0)2
- 21. How to get a model Cost Function: J(Θ) Find Θ which minimizes J(Θ).
- 22. More complicated model x1 x2 x3 x4 a1 y2 y3 Java Ruby ObjC PHP Android iOS Web I omitted bias term. y1a2 a3 a4 a5 Hidden Layer
- 23. Model Evaluation
- 24. Model Evaluation Low cost always does not mean good model.
- 25. Overfitting area (m2) price (USD) Though the cost is very low, is this a good model?
- 26. Test Data area (m2) price (USD) Give your model Test Data which were not used for training a model. Evaluate the cost against the Test Data.
- 27. Hyper Parameter Tuning •Should I add more units? •Should I add more hidden layers? •etc. X Y X Y or
- 28. Cross Validation Data When we are tuning hyper parameters, we should not evaluate them by Test Data. Otherwise, the hyper parameters will be optimized for Test Data. Then, your model will lose generalization. Split Training Data and use it as Cross Validation Data, which evaluate hyper parameters. The Test Data must be used only for final evaluation.
- 29. Entire Flow Collect Data Split Data Train and Cross Validate Model Evaluate Model
- 30. Caution This slide omits a lot of details, especially for math, and not precise. In some parts, it would be even not correct…
- 31. Demonstration