Introduction to
Machine Learning
Machine Learning – definition
A type of artificial intelligence in which computers use huge amounts of data to learn how to do tasks rather than being programmed to do them.
Machine learning makes it possible for computing systems to become smarter as they encounter additional data.
Machine Learning – types
Supervised machine learning
- Data with labels – learn the mapping between input and output.
Unupervised machine learning
- Data without labels – learn patterns in the dataset.
Reinforcement machine learning
- Feedback-based learning – actions are rewarded or punished.
Machine learning – workflow
Data collection
Different sources of data
Different types of data
Data cleaning
Data Normalization
Feature extraction
Training
Algorithm selection
Hyperparameter tuning
Testing
Statistical analysis
Prediction or classification of the new data.
Machine learning statistics
Confusion Matrix
| Predicted Positive | Predicted Negative | |
|---|---|---|
| Actual Positive | TP | FN |
| Actual Negative | FP | TN |
Sensitivity ↔︎ True Positive Rate ↔︎ Recall
Specificity ↔︎ True Negative Rate
Precision ↔︎ Positive Predictive Value
\[ Sensitivity = \frac{TP}{TP+FN} \] \[ Specificity = \frac{TN}{TN+FP} \] \[ Precision = \frac{TP}{TP + FP} \]
\[ FPR = \frac{FP}{TN + FP} = 1 - specificity \]
\[ Accuracy = \frac{TP +TN}{TP +TN +FP +FN} \]
\[ Error \text{ } rate = \frac{FP +FN}{TP + TN+ FP + FN} \]
Thank you!
manish@bioinfo.guru
manish@bioinfo.guru