Machine learning is a field of study that allows computers to learn without being explicitly programmed by improving their performance on a task through experience. It works by finding patterns in large amounts of data to optimize a performance criterion. For example, a Twitter program that watches which tweets a user labels as cyberbullying or not in order to better filter such tweets in the future is performing the task of classifying tweets (task T), and improving the number of tweets correctly classified (performance measure P) through experience (E). There are several types of machine learning including supervised learning (classification, regression), unsupervised learning (clustering), semi-supervised learning, and reinforcement learning.

1. Machine Learning

2. What is Machine Learning
• Machine Learning is the field of study that gives computers the ability to learn without being
explicitly programmed.
• 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.
• Machine learning is programming computers to optimize a performance criterion using example data
or past experience.

3. What is Machine Learning
• Suppose your twitter program watches which tweet you do or do not mark as cyberbullying, and based
on that learns how to better filter bullying tweet. What is the task T in this setting?
a. Classifying tweets as bully or no bully.
b. Watching you label tweets as bully or no bully.
c. The number (or fraction) of tweets correctly classified as bully or no bully.
d. None of the above—this is not a machine learning problem.

5. Importance of Machine Learning
• Learning general models from a data of particular examples
• Data is cheap and abundant (data warehouses, data marts); knowledge is expensive
and scarce.
• Example in retail: Customer transactions to consumer behavior
• Build a model that is a good and useful approximation to the data.

6. Types of
Machine
Learning
Supervised
• Regression
• Classification
Unsupervised
• Clustering
Semi-Supervised
• Active Learning
Case-Based Reasoning
Reinforcement Learning

7. Supervised Learning

8. Supervised Learning

9. Supervised Learning

10. Supervised Learning Example

11. Supervised Learning Example

14. Unsupervised Learning

15. Unsupervised Learning Example

19. Semi-Supervised Learning
• The crux of the semi-supervised machine learning or Active Learning
approaches is that machine learning algorithms can obtain optimum
classification accuracy with only a few labeled instances.
• These approaches are beneficial where un- labeled data can be
obtained easily in huge volumes; nonetheless, the labeling of the
collected data is difficult, laborious, and expensive.

20. Reinforcement Learning
• December, 2013
• Google bought DeepMind

21. Reinforcement
Learning
• Involvement of AI, animal psychology,
and control theory
• Reinforcement learning (RL) can learn
from experience and interaction with
the environments.
• Agent interacts with environment and
its goal is to maximize the numerical
reward