The class will cover fundamental concepts, principles, and techniques in Artificial Intelligence.
Tuesday, Thursday: 4:30 pm - 5:30 pm and by appointment
The course content will be based on the following textbook:
Attendance is highly recommended but not mandatory. It is your responsibility to obtain any information given out in class. The instructor does not give out class notes. Some material presented in the lecture is not covered by the text. Students with special needs (e.g. hearing or vision difficulties) should inform the instructor at the beginning of the semester.
Please refer to the statement on academic integrity given below.
By attending Old Dominion University you have accepted the responsibility to abide by the honor code. If you are uncertain about how the honor code applies to any course activity, you should request clarification from the instructor. The honor code is as follows:
“I pledge to support the honor system of Old Dominion University. I will refrain from any form of academic dishonesty or deception, such as cheating or plagiarism. I am aware that as a member if the academic community, it is my responsibility to turn in all suspected violators of the honor system. I will report to Honor Council hearings if summoned.”
In particular, submitting anything that is not your own work without proper attribution (giving credit to the original author) is plagiarism and is considered to be an honor code violation. It is not acceptable to copy source code or written work from any other source (including other students), unless explicitly allowed in the assignment statement. In cases where using resources such as the Internet is allowed, proper attribution must be given.
Any evidence of an honor code violation (cheating) will result in a 0 grade for the assignment/exam, and the incident will be submitted to the Department of Computer Science for further review. Evidence of cheating may include a student being unable to satisfactorily answer questions asked by the instructor about a submitted solution. Cheating includes not only receiving unauthorized assistance, but also giving unauthorized assistance.
Students may still provide legitimate assistance to one another. You are encouraged to form study groups to discuss course topics. Students should avoid discussions of solutions to ongoing assignments and should not, under any circumstances, show or share code solutions for an ongoing assignment.
Please see the ODU Honor Council’s webpage for other concrete examples of what constitutes cheating, plagiarism, and unauthorized collaboration. All students are responsible for knowing the rules. If you are unclear about whether a certain activity is allowed or not, please contact the instructor.
Lecture1: Introduction
Lecture2: Intelligent Agents
Lecture3: Problem Solving Agents
Lecture4: Uninformed Search
Lecture5: Informed Search
Lecture6: Local Search
Lecture7: Evolutionary Computing
Lecture8: Genetic Algorithms
Lecture9: Applications of Genetic Algorithms
Lecture 10: Constraint Satisfaction Problem
Lecture 11: Backtracking
Lecture12: Games
Lecture13: Pruning
Lecture14: Midterm Sample Questions
Lecture15: Knowledge Based Agents
Lecture16: Inference - Part 1
Lecture17: Inference - Part 2
Lecture18: First Order Logic
Lecture19: Learning
Lecture20: Decision Trees
Lecture21: SVM
Lecture22: Linear Regression
Lecture23: Artificial Neural Networks
Special Lecture Reinforcement Learning