1. Machine Learning
Dr. Mohamed Farouk

2. What’s in a Name Pattern Recognition Data Mining Machine Learning
Statistical Modeling
Artificial Intelligence
Computational Intelligence

3. Outline Applications of machine learning Types of modeling tasks
Machine learning algorithms

4. Handwritten Digits Recognition

5. Remote sensing image classification

6. Medical Diagnosis

7. Robot Learning

8. Facial Expression Recognition

9. Visual Object Recognition

10. Iris Flower Classification
versicolor
virginica
setosa

11. Iris Flower Classification

12. Types of Modeling Tasks
Regression
Classification
Time-Series analysis and prediction
Sequence analysis
Ranking
Anomaly Detection

13. Data f1 f2 f3 fd y X1 X2 X3

14. Regression f1 f2 f3 fd y X1 2.37 X2 4.21 X3 -1.11 0.98 0.12 f: Rd → R
f(x) = y

15. Classification f1 f2 f3 fd y X1 TRUE X2 FALSE X3 f: Rd → {c1,c2,c3}
f(x) = y

16. Ranking
R_1
R_2
R_3
B_1
B_2

17. Supervised Learning

18. Function Approximation

19. Classification
Generative model
Discriminative model

20. Labelled Training data
The Big Picture
Data Collection
Training data
Labelled Training data
Annotation
classifier
Learning algorithm
Feature Extraction
How the supervised model works
Collect Images Label/Annotate them
Feature extraction (HOG features in our case)
Apply the learning algorithm
Use the model in the detector
Processes the testing data in the detector
Evaluate the results
Test data
Evaluate

21. Types of Features Binary Numeric (discrete or continuous)
Categorical (nominal) (red, white, blue)
Ordered-Categorical (small, medium, big)
Order constraints (i.e. rank: 1st, 2nd, 3rd, …)

22. Linear Models Linear combination of features
Hyper-plane in the feature space
High bias / Low variance
Numerous methods to train, usually very fast
Categorical features need to be encoded
Very fast at runtime

23. Artificial Neural Networks

24. Artificial Neural Networks1 in
out

25. Collection of unrelated tools
Toolkits
Orange
Octave
SQL Server
WEKA
Collection of unrelated tools
Value
Ease of Use/Practicality R
Matlab
Power/Comprehensiveness

26. What you need in your toolkit
Visualization
Feature Processing/Selection
Modeling Algorithms
Meta Algorithms/Ensemble Methods
Tuning/parameter sweeps
Measurement/Visualization

27. Weka

28. Weka