Selected Research Papers & Tutorials
Required Textbook: Introduction to the Theory of Computation, 3rd
Edition, Michael Sipser, Cengage Learning, ISBN: 113318779X
Office Hours: M 10, T 15, R 15
Attendance will be taken every day. Each student is permitted two excused absences, following which their course average will be decremented 1% per absence
Course Description & Goals:
This is a mathematics course designed to explore the boundaries of knowledge in terms of information, computability and artificial intelligence. Students are expected to gain mastery level understanding of: Turing Machines, Finite State Automata, Computational Complexity, Classic Unsolved Computing Problems, Neural Networks, & Bayesian Networks.
I will randomly divide the class into groups of 2 or 3. For all group work, the entire group will be given the same grade tempered by the intra group member grade that is anonymously provided by each
student. GROUP ASSIGNMENTS
Exam Schedule (Individual):
Open Book Midterm February 27 (25%)
Take Home Final Exam April 30 (25%)
Assigned each Thursday, due the following Thursday (5%)
Every Thursday I will distribute a research paper or tutorial to be discussed the following Thursday. Research Paper Executive Summaries (2-3 pp.) from each group are then due the following
Group Project Presentations:
Each student will present a 20 minute summary of a contemporary topic in AI during the last week of class (20%)
This class will use the pedagogy of shared inquiry. Each Monday and Tuesday, we will convene class to discuss assigned reading from the textbook and other selected readings. No lectures.
Class Discussion Participation:
Each individual student’s class participation will be graded as a function of their answering and asking questions during class discussion (10%)
Chapter 0, Noam Chomsky Paper)
Chapter 1, Noam Chomsky Paper
Chapter 1, Richard Hamming Paper
Chapter 2, Turing Imitation Game Paper
Chapter 3, Godel's Incompleteness
Chapter 4, Nick Bostrom Paper,
Chapter 5, Stephen Cook Paper
Chapter 7, Artificial Neural Networks Tutorial
Chapter 7, AI Report - Individual, Pre-Approved Topic
TIMED Take Home, Individual Final Exam
The mission of Pepperdine University calls for “highest standards of academic excellence and Christian values, academic excellence.” This course embodies those standards in three ways:
● The discipline of mathematics requires the best of its students. We deepen this commitment by reading and discussing great works at the boundary of mathematics, computer science, and human knowledge.
● Christian values include the cardinal virtues: courage, wisdom, temperance, and justice. This course seeks to increase student wisdom and requires that students use courage to grapple with challenging material in order to attain its mastery.
● Finally, the pedagogy of this course requires that we learn from each other as well as from the scientific voices of the past and present. This affirms our desire to build and maintain an academic community,
which means we wish to employ the best practices of the life of the mind.