1. Machine Learning
Week 1

2. Machine Learning
Machine Learning develops algorithms for making predictions from data
Part of Statistics

3. Machine Learning

4. Data Data consists of data instances
Data instances are represented as feature vectors
180 70
120 80
110 90
Features are chosen for a specific task at hand (Feature Engineering)

5. Machine Learning is Generalization of a specific task
Making predictions about new data instances
- Data A consists of 26 coherent groups
This data instance belongs to group #18.

6. Machine Learning consists of
Classification
Clustering
Regression

7. Classification Training phase
- Input: data instances and their true labels
-output: the classification model” or “classifier”
Testing Phase
- Input: a data instance
- output: Its label

8. Example
Systolic BP
Negative instances
Positive instances HR

9. K-Nearest-Neighbors Classifier
Systolic BP
Negative instances
Positive instances HR

10. Support Vector Machine (SVM)
Systolic BP HR

11. SVM can be Nonlinear separable classifier
Systolic BP HR

12. SVM can be multi-class classifier
Systolic BP HR

13. Decision Trees