Introduction to Machine Learning concepts
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A brief introduction of the main Machine Learning concepts, including supervised and unsupervised learning, and reinforcement learning
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Techniques covered for Regression:
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- Polynomial Regression
Techniques covered for Classification:
- Simple and Multiple Logistic Regression
- Linear and Quadratic Discriminant Analysis
- K-Nearest Neighbors
Clustering:
- K-Means clustering
- Hierarchical clustering
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Introduction to Machine Learning concepts
- 2. 1958
- 3. 1998
- 9. &
- 11. Supervised orange banana banana apple apple apple apple Trained model New «unseen» data orange Given labeled examples learn to predict new examples
- 13. Is him a genuine or an impostor?
- 14. Which brand the car belongs to?
- 15. At which price should I sell my beautiful house? 500’000 € ? 410’000 € ? 279’000 € ?
- 16. Unsupervised Given data, but not labels, learn to cluster/group similar data
- 17. $$$ $$$ $$$ $ $ $
- 19. Reinforcement Given a sequence of examples/states and a reward after completing that sequence, learn to predict the action to take in for an individual example/state … Win … Lose
- 20. Why is Machine Learning possible?
- 21. Why is Machine Learning possible? MORE DATA AVAILABLE – LARGER MEMORY IN HANDLING THE DATA – GREATER COMPUTATIONAL POWER FOR CALCULATING – ONLINE CONTINUOUS LEARNING
- 22. How do we do Machine Learning? Data gathering MINING SOFTWARE REPOSITORIES – INTERVIEWS – SURVEYS – ONLINE OPEN DATASETS – STREAM SOURCES Data cleanliness and quality BIGGER IS NOT BETTER!
- 23. How do we do Machine Learning? Knowledge representation Data formatting VECTORS – MATRICES Data visualization IT IS OFTEN A LOT OF DATA NEED FOR SPECIALIZED SOFTWARE!
- 24. How do we do Machine Learning? Strategy selection …
- 25. Strategy selection: The machine learning framework f( )=apple f( )=orange f( )=410’000 € Apply a prediction function to a feature representation of the data to get the desired output
- 26. y = f(x) OUTPUT PREDICTION FUNCTION FEATURE Training Given a training set of labeled examples 𝑥1, 𝑦1 , … , 𝑥𝑛, 𝑦𝑛 estimate the prediction function f by minimizing the prediction error on the training set Testing Apply f to a never before seen test example x and output the predicted value y=f(x)
- 27. Strategy selection: The machine learning design phase Training data Features Training Learned model Training labels Features Learned model Prediction Test data
- 28. How do we choose training and test set? Two most common techniques are percentile sampling ad k-folds cross-validation
- 29. Percentile sampling Divide the dataset between X% to be used for training and Y% to be used for prediction where X>>Y (e.g., 70/30) Training observation Testing observation
- 30. K-folds cross-validation Randomly divide dataset into k “folds” then randomly select one to be used as testing data. Circularly select another one, selecting all of them at least once, calculate the average error rate of estimations 3 folds Train on Test on Train on Test on Train on Test on
- 32. In summary ML means learning from the past to predict the future MACHINES NEED TO MAKE LESS ERROR IN PREDICTION (ERROR IS EVALUATED WITH STATITSICAL INDEXES AS F-MEASURE) At least 3 classes of ML exist SUPERVISED LEARNING UNSUPERVISED LEARNING REINFORCEMENT LEARNING