Finite Automata

Formalizing Computation

Limits of Computation

Limits of Human Reasoning

Computational complexity

Time Complexity

Graph Theory

P vs NP

Randomized Algorithms

Cryptography

CS 251 Great Ideas in Theoretical Computer Science Carnegie Mellon University Fall 2022 A course offering rigorous study of computation, examining the central results and questions about the nature of computation, including finite automata, computational complexity, and cryptography. This course is about the rigorous study of computation, which is a fundamental component of our universe, the societies we live in, the new technologies we discover, as well as the minds we use to understand these things. Therefore, having the right language and tools to study computation is important. In this course, we explore some of the central results and questions regarding the nature of computation.   

CS 251 Great Ideas in Theoretical Computer Science

Theoretical Computer Science

Set theory

Mathematical Proofs

Indirect Proofs

Propositional calculus (Propositional logic)

First-order logic

Function (computer programming)

Graphs

Mathematical Induction

Regular Expressions

CS 103: Mathematical Foundations of Computing Stanford University Winter 2023 CS 103 introduces mathematical logic, proofs, and discrete structures, paving the way to an understanding of computational problem-solving. It encourages a profound appreciation of mathematical beauty while addressing concepts like finite automata and regular expressions. CS106B is a prerequisite or corequisite. The course also incorporates programming assignments. Are there “laws of physics” in computing? Are there fundamental restrictions to what computers can and cannot do? If so, what do these restrictions look like? What would make one problem intrinsically harder to solve than another? And what would such restrictions mean for our ability to computationally solve meaningful problems?

In CS103, we'll explore the answers to these important questions. We'll begin with an introduction mathematical logic, proofs, and discrete structures (sets, relations, functions). These mathematical tools will enable the real heart of the course, which is to rigorously answer questions like “what does it mean for a computer to solve a problem?” and “what makes some problems (sorting) inherently harder than others (searching)?”

In the course of the quarter, you'll see some of the most impressive (and intellectually beautiful) mathematical results of the last 150 years. In some ways, I like to think of this course as a course in both art appreciation and practice. I’ll bring you through a gallery and show you some of my favorite achievements of mathematical artistic beauty, and like a good tour guide help you understand what is special about what you’re looking at. You’ll also need to pick up the paintbrush yourself and write some proofs of your own. You'll learn how to think about computation itself and how to show that certain problems are impossible to solve. Finally, you'll get a sense of what lies beyond the current frontier of computer science, especially with regards to biggest open problem in math and computer science, the P = NP problem.  CS103 has CS106B as a prerequisite or corequisite. This means that if you want to take CS103, you must either have completed or be concurrently enrolled in one of CS106B or CS106X (or have equivalent background experience).

Over the course of the quarter, we will be giving out a number of programming assignments to help you better understand the concepts from the course. Those assignments will assume a familiarity with C++ and programming concepts (especially recursion) at a level that’s beyond what’s typically covered in CS106A. The timing on these assignments is designed so that they’ll sync up with what’s covered in CS106B.

Although CS103 is a course on the mathematical theory behind computer science, the only actual math we'll need as a prerequisite is high-school algebra. We'll build up all the remaining mathematical machinery we need as we go. We've released another handout detailing the mathematical prerequisites for this course, so if you have any questions, check it out and see what you find! ### Readings

There are two recommended textbooks for this quarter. The first is How to Read and Do Proofs by Daniel Solow, which is a great resource for learning how to approach mathematical problem-solving. The second is Introduction to the Theory of Computation, Third Edition by Michael Sipser. You might find this book useful in the second half of the quarter. We will never directly test material available only in the textbooks; the course materials we provide will be all you need.

There are copies of each of these books in reserve in the Engineering Library.

A helpful note from the School of Engineering:

*“All students should retain receipts for books and other course-related expenses, as these may be qualified educational expenses for tax purposes. If you are an undergraduate receiving financial aid, you may be eligible for additional financial aid for required books and course materials if these expenses exceed the aid amount in your award letter. For more information, review your award letter or visit the [Student Budget website](https://financialaid.stanford.edu/undergrad/budget/index.html).”*

CS 103: Mathematical Foundations of Computing

Mathematical Foundations

Deterministic finite automaton

Finite Automata

2 courses cover this concept

CS 251 Great Ideas in Theoretical Computer Science

CS 103: Mathematical Foundations of Computing