Machine Learning – Introduction (Week 1)

- An area of Artificial Intelligence

- Examples:

- Database mining

Large datasets from growth of automation/web.
E.g., Web click data, medical records, biology, engineering

- Applications can’t program by hand.

E.g., Autonomous helicopter, handwriting recognition, most of
Natural Language Processing (NLP), Computer Vision.

- Self-customizing programs

Definition:

1. Field of study that gives computers the ability to learn without being explicitly programmed.

2. 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.

Types of Machine Learning:

- Supervised Learning: Right answer is given

     - Regression: Predict continuous valued output

          - Linear Regression

               - Univariate (One variable)

               - (Multiple variables)

          - Polynomial

     - Classification (logistic Regression)

- Unsupervised Learning: Computer finds data and segments

     e.g. Organize computer clusters, Social network analysis, Market segmentation, Astronomical data analysis, Cocktail party problem etc.

- Reinforcement Learning

- Recommender System