CSI 5310: Introduction to Computation Theory — Fall 2022
This course is an introduction to the theory of computation. It covers formal models of computation, computability, complexity, and related topics. This course forms a foundation for much of the subsequent work/research you will do in computer science. As you'll see, computation theory is fascinating because there are problems which we can describe simply which have not yet been solved.
The basic topics covered in this course are:
- formal models of computation ("how can we define computation in a mathematical way?")
- DFAs, NFAs, regular languages; CFGs, context free languages; Turing machines
- computability theory ("what sorts of problems can computers solve?")
- decidability, reductions
- complexity theory ("how efficiently can computers solve different problems?")
- time complexity, complexity classes, P versus NP, NP-completeness
- space complexity; intractability; randomized classes of computation
This is a difficult course. Be prepared to invest the time necessary to understand the concepts, and to do the assignments. My best advice is to pay attention during the lectures, ask questions, and start assignments early.
Besides this page, most of the course information is available in Canvas. Please see that site for the course schedule, readings, lectures, assignments, discussions, etc.
We will use the scheduled class meeting times to discuss course material. Before each meeting, you should have viewed the lectures for the week, and come prepared to discuss the material, driven by your questions.
My office location and office hours are listed on my home page. I am glad to talk to students during and outside of office hours.
SchedulePlease see Canvas for a schedule of the course.
Textbooks & resources
Required text: we will be using Michael Sipser's textbook Introduction to the Theory of Computation (3rd Edition). Please note: using another edition will probably be fine, but it may have different numbering of problems and exercises. I have seen students lose credit for using "international" versions that are mostly the same, except for the problems and exercises. Make sure you are doing the problems as assigned.
Further online resources:
- We will use Canvas for keeping records of assignment grades.
- Please see this STL cheatsheet (1) and this STL cheatsheet (2) for a quick overview of the STL.
- LaTeX information: LaTeX introduction, another LaTeX introduction, LaTeX reference
- Here is a C++ style guidelines for the class. These guidelines also apply to Java.
Grades will be assigned based on this breakdown:
- assignments: 50%
- midterm exam 1: 15%
- midterm exam 2: 15%
- final exam: 20%
Final letter grades will be assigned at the discretion of the instructor, but
here is a minimum guideline for letter grades:
F < 60 ≤ D- < 62 ≤ D < 67 ≤ D+ < 70 ≤ C- < 72 ≤ C < 78 ≤ C+ < 72 ≤ C < 78 ≤ C+ < 80 ≤ B- < 82 ≤ B < 88 ≤ B+ < 90 ≤ A- < 92 ≤ A
Some homeworks may be worth more than others. All exams are closed-book. The final will be comprehensive.
Homeworks should be written up in (nice-looking) LaTeX. Homeworks are due at the beginning of class on the due dates for full credit. Homeworks turned in after I have collected them but before the end of class will receive a 20% penalty. No homeworks will be accepted after class on the due date.
Students receiving graduate credit for this course will be required to complete additional components of several homework assignments. These components will give the advanced student an opportunity to explore topics, to implement algorithms, and to apply techniques that are not normally covered by undergraduates in this course. Scores on these additional components will be included in the homework assignment portion of the grade. Students receiving graduate credit will also have additional exam questions on these advanced topics, and the scores for these extra questions will be included in the examination portion of the grade. The set of topics for graduate credit include:
- the recursion and fixed-point theorems
- Kolmogorov complexity
- hierarchy of undecidability
- logic, decidability, and incompleteness
- space complexity
- randomized complexity classes
- Check this website and your email regularly for updates and announcements. It is your responsibility to stay current on course changes.
- All work in this course is strictly individual, unless the instructor explicitly states otherwise. While discussion of course material is encouraged, collaboration on any work for the course is not allowed. Collaboration includes (but is not limited to) discussing with anyone other than the professor any material that is specific to completing an assignment. You are not to work with anyone else on any assignment unless I expressly permit it. You are encouraged to discuss the course material with the professor, preferably in office hours, and also by email.
- Baylor's ECS policy requires 75% class attendance from each student. Even "excused" absences are included in the overall absent count. If a student attends less than 75% of the classes, he or she will automatically fail the course.
- Homeworks which are late are not accepted. Exams are the only things which may be made up with prior arrangement (made at least one class before to the exam) or due to illness, with a note from a health care professional.
- Bring any grading correction requests to my attention within 2 weeks of receiving the grade or before the end of the semester, whichever comes first. After that, I will not adjust your grade. If you find any mistake in grading, please let me know.
Plagiarism or any form of cheating involves a breach of student-teacher trust. This means that any work submitted under your name is expected to be your own, neither composed by anyone else as a whole or in part, nor handed over to another person for complete or partial revision. Be sure to document all ideas that are not your own. Instances of plagiarism or any other act of academic dishonesty will be reported to the Honor Council and may result in failure of the course. Not understanding plagiarism is not an excuse. I expect you as a Baylor student to be intimately familiar with all aspects of the Honor Code.
I take academic honesty very seriously. Many studies, including one by Sheilah Maramark and Mindi Barth Maline have suggested that "some students cheat because of ignorance, uncertainty, or confusion regarding what behaviors constitute dishonesty" (Maramark and Maline, Issues in Education: Academic Dishonesty Among College Students, U.S. Department of Education, Office of Research, August 1993, page 5). In an effort to reduce misunderstandings in this course, a minimal list of activities that will be considered cheating have been listed below.
- Copying another student's work. Simply looking over someone else's source code is copying.
- Providing your work for another student to copy.
- Collaboration on any assignment, unless the work is explicitly given as collaborative work.
- Using notes or books during any exam.
- Giving another student answers during an exam.
- Reviewing a stolen copy of an exam.
- Studying tests or using assignments from previous semesters.
- Providing someone with tests or assignments from previous semesters.
- Taking an exam for someone else.
- Turning in someone else's work as your own work.
- Studying a copy of an exam prior to taking a make-up exam.
- Providing a copy of an exam to someone who is going to take a make-up exam.
- Giving test questions to students in another class.
- Reviewing previous copies of the instructor's tests without permission from the instructor.
Title IX Office
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This page was last updated August 16, 2022 at 11:25 (America/Chicago)