Course Description:
Theoretical computing models and the formal languages they characterize: finite state machines, regular expressions, pushdown automata, context-free grammars, Turing machines and computability. Capabilities and limitations of each model, and applications including lexical analysis and parsing.

Prerequisites:

• either CS 2302 Data Structures and either Discrete Mathematics or Discrete Structures, with grades C or higher,
• or CS 2401 Elementary Data Structures and either Discrete Mathematics or Discrete Structures, with grades B or higher.

Textbook:
Reading and laboratory assignments will be drawn from Introduction to the Theory of Computation, by Michael Sipser (both 2nd and 3rd editions are OK). This book is available at the bookstore and through major online book retailers, and you are expected to acquire a copy for your use in this course. Photocopied textbooks are illegal and their use will not be tolerated.

The purpose of the exams is to allow you to demonstrate mastery of course concepts. Make-up exams will be given only in extremely unusual circumstances. If you must miss an exam, please meet with an instructor, BEFORE the exam if at all possible.

Grading:

• first test: 10
• second test: 10
• third test: 10
• home assignments: 20
• in class quizzes and exercises: 20
• final exam: 30

The nominal percentage-score-to-letter-grade conversion is as follows:

• 90% or higher is an A
• 80-89% is a B
• 70-79% is a C
• 60-69% is a D
• below 60% is an F

Homework Assignments: Homework and lab assignments are designed to allow you to practice using the concepts presented in lecture and in your reading. Homework and lab assignments may include written problems, tutorial exercises, and programming problems. Assignments usually will be due at the start of the next class. Late homework will be accepted only in unusual circumstances, by prior arrangement if at all possible.

Homework must be done individually. While you may discuss the problem in general terms with other people, your answers and your code should be written and tested by you alone. If you need help, consult a TA or a professors.

Quizzes: The purpose of a quiz is to ensure that you have read the weekly reading assignment and to verify that you have mastered the major concepts of recent lectures. Quizzes typically will be about 5-10 minutes in length and will cover the material assigned to be read for the upcoming lecture plus selected concepts from previous lectures. Although we will ignore a small amount of missed or failed quizzes, there will be no make-up on missed quizzes.

Special Accommodations: If you have a disability and need classroom accommodations, please contact the Center for Accommodations and Support Services (CASS) at 747-5148 or by email to cass@utep.edu, or visit their office located in UTEP Union East, Room 106. For additional information, please visit the CASS website at http://www.sa.utep.edu/cass. CASS's staff are the only individuals who can validate and if need be, authorize accommodations for students.

Scholastic Dishonesty: Any student who commits an act of scholastic dishonesty is subject to discipline. Scholastic dishonesty includes, but not limited to cheating, plagiarism, collusion, submission for credit of any work or materials that are attributable to another person.

Cheating is:

• copying from the test paper of another student;
• communicating with another student during a test to be taken individually;
• giving or seeking aid from another student during a test to be taken individually;
• possession and/or use of unauthorized materials during tests (i.e. crib notes, class notes, books, etc.);
• substituting for another person to take a test;
• falsifying research data, reports, academic work offered for credit.

• using someone's work in your assignments without the proper citations;
• submitting the same paper or assignment from a different course, without direct permission of instructors.

Collusion is unauthorized collaboration with another person in preparing academic assignments.

Instructors are required to -- and will -- report academic dishonesty and any other violation of the Standards of Conduct to the Dean of Students.

NOTE: When in doubt on any of the above, please contact your instructor to check if you are following authorized procedure.

Faculty Information:
Professor: Luc Longpré
Office: 3.0420 CCS building
Phone: 747-6804
e-mail: longpre @ utep . edu
Office Hours: MW 3-4pm. Send an e-mail during that time and I will call you on Microsoft Teams.
See https://www.utep.edu/cs/people/longpre.html, select "Student appointments" for instructions on how to make appointments at other times.

Teaching Assistant (TA):
Abram Nguyen, email amnguyen2@miners.utep.edu, office hours MW 12noon-1pm, TR 4-5pm on blackboard collaborate in the Course Room.

Instructor of another section of Automata:
Vladik Kreinovich, email vladik@utep.edu, office hours MW 10:30-11:30 am, 12:30-1:30 pm.

Major Topics Covered in the Course:

• Regular languages, finite automata, non deterministic FA
• Context-free languages, pushdown automata
• Parsing, normal forms, ambiguity
• Pumping lemmas and closure properties
• Turing machines and other equivalent models
• Decidable languages, non-decidable languages, recognizable languages, Chomsky hierarchy

Level 1: Knowledge and Comprehension
Level 1 outcomes are those in which the student has been exposed to the terms and concepts at a basic level and can supply basic definitions. The material has been presented only at a
superficial level.
Upon successful completion of this course, students will:

1a. Be familiar with the implications of Church-Turing thesis.

1b. Understand that there are problems for which an algorithm exists, and problems for which there are no algorithms (non-recursive, non-recursively enumerable languages) and understand the implications of such results.

1c. Understand and explain the diagonalization process as used in proofs about computability.

1d. Understand the difference between feasible and non-feasible algorithms, understand the limitations of the current formalization of feasibility as polynomial-time.

1e. Understand the main ideas behind the concepts of NP and NP-hardness, know examples of NP-hard problems.

Level 2: Application and Analysis
Level 2 outcomes are those in which the student can apply the material in familiar situations, e.g., can work a problem of familiar structure with minor changes in the details.
Upon successful completion of this course, students will be able to:

2a. Convert a non-deterministic FA (respectively transition graph) into an equivalent deterministic FA.

2b. Convert a transition graph or NFA into an equivalent regular expression.

2c. Convert a regular expression into an equivalent FA.

2d. Construct a regular expression (respectively a context-free grammar) for a regular language (respectively context-free language).

2e. Convert a context-free grammar into an equivalent pushdown automaton.

2f. Construct a context-free grammar for a given context-free language.

2g. Design an algorithm for a machine model to simulate another model.

2h. Build simple Turing machines.

2i. Prove formally properties of languages or computational models.

2j. Apply a parsing algorithm.

2k. Build a parse tree or a derivation from a context-free grammar.

2l. Use the closure properties in arguments about languages.

Level 3: Synthesis and Evaluation
Level 3 outcomes are those in which the student can apply the material in new situations. This is the highest level of mastery.
Upon successful completion of this course, students will be able to:

3a. Compare regular, context-free, recursive, and recursively enumerable languages.

3b. Compare FA, PDA, and Turing machines.

Daily Schedule: To be distributed separately.