1. Artificial Intelligence, P.II
Robbie Nakatsu
AIMS 2710

2. Machine Learning
Field of study (a subfield of AI) that gives computers the ability to learn without being explicitly programmed (Arthur Samuel, 1959)
Example: Checkers playing program that sees tens of thousands of examples of board positions, and learns over time what the good positions are.
Machine Learning in Data Science is about learning from data: the analysis of data to find useful or informative patterns.

3. Types of Machine Learning Problems
Regression problems (predicting continuous-valued outcome)
Predicting price of home from its size
Predicting price of home based on multiple variables (size, year built, location, condition of the building, etc)
Classification problems (predicting discrete-valued outcome)
Determining malignancy of a tumor based on its size
Determining malignancy of a tumor based on multiple variables (size, age of patient, uniformity of tumor, etc.)

4. Housing Price Prediction
in 1000’s
Size in feet2
Regression: predict continuous valued output (price) from home size

5. Understanding Regression
Regression is concerned with specifying the relationship between a single numeric dependent variable (the value to be predicted) and one or more numeric independent variables (the predictors).
Fit the best line:
y = β0 + β1x
Fit the best quadratic equation:
y = β0 + β1x + β2x2
In the previous example, y is the price, and x is the size of the home.

6. Housing Price Prediction

7. Tumor Malignancy Prediction
X’s represent malignant cases
O’s represent benign cases
Classification: predict discrete valued output (tumor malignancy) from age and tumor size

8. Examples of Prediction Problems
A credit card company wants to predict whether a credit card transaction is fraudulent or not.
A company that sells ice cream wants to predict how much ice cream to produce over the summer months (June – August).
A software company wants to design an spam filter to predict whether an is spam or not.
A marketing researcher has customer data and wants to predict who among the customers are the most profitable.
Which of the above are classification problems and which are regression problems?

9. A Neural Network
is a type of machine learning that is patterned after the human brain. It is capable of learning to recognize patterns and relationships in the data it processes.
A neural network can simulate the human ability to classify things based on the experience of seeing many examples. Neural networks must first be “trained” to recognize the patterns.

10. A Neural Network can perform pattern recognition tasks like:
Reading handwriting
Speech recognition
Detecting abnormal patterns in electrocardiographs
Image analysis/object recognition
All the above are examples of classification tasks involving noisy data.

11. Patterned after the human brain: brain cells, or neurons, interconnected in a network

12. An Intelligent Agent
is an artificial intelligence system that can move around your computer or network performing repetitive tasks independently, adapting itself to your preferences.
An intelligent agent is like a travel agent in that it performs tasks that you stipulate.

13. Examples Intelligent search engines Personal assistants
Search engines that know who you are, your preferences, where you are, who your friends are, etc.
Personal assistants
Check and filter your s
Search the web and collect important news items for you

14. Intelligent Agent Characteristics
Autonomy
Adaptivity
Sociability

15. A Robot That Lacks Autonomy

16. Autonomous Cars
Minority Report Spiders

17. The Promise and Limitations of AI
IBM’s Deep Blue (AI program that beat the world chess champion) and Watson (Jeopardy playing computer) were crowning AI achievements that represented milestones in intelligent computing.
However, we have no computers that are capable of “thinking” like humans.

18. Augmented Intelligence: Partnering Machine with Man
Creating human-computer partnerships may be more powerful than the pursuit of machines that can think on their own. Under this view, AI does not become a replacement for humans.
Humans are better than computers at performing certain tasks. Computers are good at processing a lot of data very quickly.

19. Example: Partner Watson with Human Doctors
One project involved using the machine to work in partnership with doctors on cancer treatment plans.
The Watson system was fed more than 2 million pages from medical journals, and could search up to 1.5 million patient records.
The system provides recommendations, but doctors can critique the recommendations based on their own intuition, experience, and know-how.

20. Recap and Summary Types of decisions Decision Support Systems
OLAP (online analytical processing)
Supporting groups with technology
Expert Systems
Machine Learning including Neural Networks
Intelligent Agents
Human-Computer partnerships are the future of AI