Course Information
Primary textbook: Artificial Intelligence A guide to Intelligent Systems; Second Edition; Michael Negnevitsky
Secondary textbook: Machine Learning; Tom M. Mitchell
1.1 Quizzes, Assignments, Exams, Projects, Etc.
There will be 3 quizzes on the following material:

Overview and Classic AI

Statistical and Fuzzy Systems

Neural Networks and Evolutionary Systems
The final exam will have two parts. If you have a 93% average on your work prior to the final, your final will have one part, otherwise 2 parts.
There will several homework assignments, and 3 or more programming projects.
If you do all your work on time and study for the quizzes you should do well.
1.2 Grading
The grade will be based on a weighted average. Quizzes and the final will comprise 60% of the grade, homework, programs and projects fill out the remaining points.
A university grade scale will be used:
A 90 – 100
B 80 – 89
C 70 – 79
D 60 – 69
F < 60
There will little to no curving done. Only in certain cases, for example if a student has a 79.5 average, and did all their work on time, they are likely to be bumped from a C to a B. If on the other hand a student has a 79.8 average, smoked the tests, but did not turn in all of their assignments, they will receive a C.
On another note, quality of work will also be taken into consideration. An A will be awarded for work that it of excellent quality and fulfills all requirements. If work is turned in that is of excellent quality and goes beyond the requirements this will be noted and will definitely be taken into account in the grading process.
Miscellaneous
Learning is like many other endeavors in that practice and repetition aid in the process. This class will present several topics. It is important to be clear on the concepts that are being taught and it is equally important to put those concepts to practical use through various homework assignments and projects. The homework assignments and projects help by tying together the concepts learned in the classroom in a practical manner.
For an average student, an easy rule of thumb to ensure an “A” is that for every hour spent in class, 3 hours should be spent outside of class. So for this class, spend about 9 hours a week outside of class and you should get an “A”. If it takes time, that is almost better. Difficult material will help you learn strategies to deal with complex material. Many times gifted students have real trouble when they finally run into material that is hard for them. This is because they have never experienced cases in which they don’t what to do, so they have a difficult time developing a strategy to find answers.
Introduction
Read chapter 1
Objective:

Gain an overview of knowledge about the field.
Assignments:

Page 21, Questions 2, 4, 5, 6, 7, 8, 10, 11
2.1 Definitions:

Intelligence – the ability to understand and learn things.

Understand – analyze

Learn – create a behavior in order to cope with a new situation.

Intelligence – the ability to think and understand instead of doing things by instinct or automatically.

The second of these two definitions imply that there is local adaptation and learning occurring rather than preprogrammed or generational learning.

Many behaviors that bugs, animals, and people exhibit are inherited. For example, deer and horses begin to walk within a short time from being born.

Intelligence – The ability to learn and understand, to solve problems and to make decisions.

Thinking is the activity of using your brain to consider a problem or to create an idea. (page 1)

Can something without a brain think?

Does something have to be alive to think?

If something thinks, is it alive?
2.2 History:

Alan Turing – Alan Turing, “Computing Machinery and Intelligence”, 1950.

Proposed the concept of a universal machine (Turing Machine)

Helped break codes in WWII.

Designed “Automatic Computing Engine”

Wrote the first program that could play chess.

Some key questions that he asked – still relevant today

Is there thought without experience?

Is there intelligence with life?

Turing game – Can a computer communicate with a person so well that the person cannot tell whom they are talking with, computer or person?

Turing thought that by the year 2000 computers would pass this test.

Early History (1943 – 1956)

Warren McCulloch and Walter Pitts designed a model of human nerves in which each neuron was either in an on state or off state. They showed that there model was equivalent to a Turing machine and showed some basic structures.

Claude Shannon showed the need for heuristics in order to solve complex problems.

Heuristic – (book definition) A strategy that can be applied to complex problems; it usually  but not always – yields a correct solution. Heuristics, which are developed from years of experience, are often used to reduce complex problem solving to more simple operations based on judgement. Heuristics are often expressed as rules of thumb.

What does this definition imply in terms of the solution space?

What does it imply in terms of solution optimality.

How does this compare with a search algorithm or greedy algorithm?

Heuristic search – A technique that applies heuristics to guide the reasoning and thus reduce the search space for a solution.

Algorithm – (American Heritage Dictionary) A rule or procedure for solving a problem.

What does this definition imply?

Under defined circumstances, an algorithm will find a correct solution.

Optimal Solution – The best solution to a problem. Many algorithms that find optimal solutions have to check every solution in some way. When the solution space is large, this can be prohibitive.

Claude Shannon’s 1950 paper on chess playing programs pointed out the number that a typical chess game had about 10^{120} possible moves.

How long would it take a computer to pick the first move if it could evaluate 1 move in every time cycle and the computer were a 10 gigahertz (10^{9 }evaluation cycles per second) machine?

Other notable scientists:

John McCarthy

John Von Neumann

Marvin Minsky

Middle History (Great expectations)

John McCarthy develops LISP

Marvin Minsky focuses on formal logic

McCulloch and Pitts neural networks further developed by Rosenblatt (perceptrons)

Approach to solving problems:

General methods (Weak methods)

Reality sets in (late 60’s and early 70’s)

Expert systems show success – contrast with weak methods.

Neural networks rebirth

Evolutionary Computation
2.3 Summary 
What is Artificial Intelligence – AI is the study of making machines think.

Two main branches of AI

Classic AI techniques  rooted in heuristics, symbolic computing and expert systems.

Chess playing – Alan Turing

Mycin, Prospector – expert systems developed at Stanford

Symbolic processing  Deterministic searching of solution spaces.

Machine Learning techniques – numerically based, neural networks, nondeterministic solution space searching (genetic algorithm, simulated annealing.

McCulloch/Pitts neurons

Nondeterministic searching – genetic algorithm, simulated annealing, evolutionary programming.
