Mathematical Thinking in Computer Science

Start Date: 07/05/2020

Course Type: Common Course

Course Link:

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About Course

Mathematical thinking is crucial in all areas of computer science: algorithms, bioinformatics, computer graphics, data science, machine learning, etc. In this course, we will learn the most important tools used in discrete mathematics: induction, recursion, logic, invariants, examples, optimality. We will use these tools to answer typical programming questions like: How can we be certain a solution exists? Am I sure my program computes the optimal answer? Do each of these objects meet the given requirements? In the course, we use a try-this-before-we-explain-everything approach: you will be solving many interactive (and mobile friendly) puzzles that were carefully designed to allow you to invent many of the important ideas and concepts yourself. Prerequisites: 1. We assume only basic math (e.g., we expect you to know what is a square or how to add fractions), common sense and curiosity. 2. Basic programming knowledge is necessary as some quizzes require programming in Python. Do you have technical problems? Write to us:

Course Syllabus

Why some arguments are convincing and some are not? What makes an argument convincing? How to establish your argument in such a way that there is no possible room for doubt left? How mathematical thinking can help with this? In this week we will start digging into these questions. We will see how a small remark or a simple observation can turn a seemingly non-trivial question into an obvious one. Through various examples we will observe a parallel between constructing a rigorous argument and mathematical reasoning.

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Course Introduction

Mathematical Thinking in Computer Science Mathematical thinking is important! It is often the case that people (students in science classes) have no real background in math, and it can be very frustrating. This course will provide an introduction to mathematical modeling in the context of computer science. We will introduce a number of models, and we will discuss how these models interact with each other, for example, in terms of graph theory. We will then focus on the use of formal notation in computing, and discuss key concepts like equality, composite, and partial derivatives. We will then move into more advanced topics including automorphisms, extremes, and general purpose limitations, and the equivalences between numeric and logical models. This is a very basic course, and it is intended to be fun and easy. We hope you will take some notes (some computer science is hard!). If you are looking for general purpose thinking in computer science, or if you just want to brush up on your skills, this is a great course to start with! Course Overview video: The videos in this and the previous course (Mathematical Modeling in C) are the same video as in lecture 1.1, but with more diagrams and pictures. The text is the same, just formatted differently. Links are still available at the end of each lesson, but they are in a much shorter format

Course Tag

Mathematical Induction Proof Theory Discrete Mathematics Mathematical Logic

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