Just a overview
Machine Learning definition: Field of study that gives computers the ability to learn without being explicitly programmed (Arthur Samuel, 1959).
Well-posed Learning Problem: A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E (Tom Mitchell, 1998)
Machine learning algorithms:
- Supervised Learning
- Unsupervised Learning
- Others: Reinforcement learning, recommender systems.
Supervised Learning
Means that you already know the right answer, you train the computer to give you the answer that match to the right answer.
Supervised Learning Has Labels.
- Regression: Predict continuous valued output, i.e., predicts unknown or missing values
- Classification: Discrete valued output (0, or 1, or 2 …..)
trying to map the input variable into discrete categories
Unsupervised Learning
The class labels of training data is unknown
- Clustering
- Non-clustering
ML-Based Data Mining
Simple theory hierarchy of ML-based quantitative data mining
Reference
Andrew Ng. https://www.coursera.org/learn/machine-learning
Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques (3rd ed.). Burlington: Elsevier Science.