The Complete Mathematics Software Developer Course For 2022

All Mathematics You Need To Know As a Programmer

What you'll learn

Proof Techniques. Mathematical Induction and Recursion Theory.

Mathematical Logic. Propositional and First Order Calculus. Model Theorem.

Programs verifications and Model Checking

Linear Algebra. Matrix Theory in Computer Science.

Boolean Algebra and its applications in Digital Electronics.

Lambda Calculus as a Foundation of Functional Programming

Number Theory and Encryption.

Modern Statistics and Probabilistic Methods in Computer Science.

Functional Analysis and the efficiency of computer algorithms Decision Theory

Requirements

Desire to Learn Mathematics for Programming

Interested in Computer Science Field

Basic High School Mathematics

Description

This course covers all Mathematics needed to become Software Developer. Here we will discuss Linear Algebra, Modern Analysis, Mathematical Logic, Number Theory and Discrete Mathematics. By the end of this course you will be able to analyze and describe computer science concepts and methods. This course is a great opportunity for you to gain deep understanding of all processes a executed in the computer system when programming. The specific objectives of the course are the following:Learn how to apply proof techniques to your computer program. Learn encrypting and decrypting messages with Number Theory. Learn how the software development is related to Discrete Mathematics and Digital Electronics. Understand how to use mathematical tools to properly analyze any computer algorithm.Learn how to apply Calculus, Probability Theory and Linear Algebra while computing. Understand how to apply Lambda Calculus to Functional Programming. Discrete mathematics is the study of mathematical structures that are fundamentally discrete rather than continuous. In contrast to real numbers that have the property of varying "smoothly", the objects studied in discrete mathematics – such as integers, graphs, and statements in logic – do not vary smoothly in this way, but have distinct, separated values.Discrete mathematics therefore excludes topics in "continuous mathematics" such as calculus or Euclidean geometry. Discrete objects can often be enumerated by integers. More formally, discrete mathematics has been characterized as the branch of mathematics dealing with countable sets (finite sets or sets with the same cardinality as the natural numbers). However, there is no exact definition of the term "discrete mathematics." Indeed, discrete mathematics is described less by what is included than by what is excluded: continuously varying quantities and related notions.

Overview

Section 1: Introduction

Lecture 1 Why Learning Mathematics for Computer Science?

Section 2: Boolean Variables Logic

Lecture 2 Boolean Variables

Lecture 3 Truth Tables

Lecture 4 De Morgan's Law

Lecture 5 Boolean Exercise - Solution

Section 3: Boolean Algebra for Digital Electronics

Lecture 6 Boolean Operations in Computer Hardware

Lecture 7 Computer Transistors and Gates

Lecture 8 Circuit Representation and Exercise

Lecture 9 Circuit Representation: Exercise Solution

Lecture 10 Simplification of Logical Circuits

Lecture 11 Set Reset Flip - Flop

Lecture 12 Logical Circuits and SR Flip-Flop: Exercise Solution

Section 4: Numerical Systems and Their Applications

Lecture 13 Decimal Numerical System

Lecture 14 Binary Numerical System

Lecture 15 Two's Component Notation

Lecture 16 Hexadecimal Numbers

Section 5: Digital Representations and Error Detection

Lecture 17 Representation of Characters and Numerical Values

Lecture 18 Digital Representation of Sounds

Lecture 19 Digital Representation of Images

Lecture 20 Error-Correction in the Digital Systems

Section 6: Set Theory

Lecture 21 Sets Relations

Lecture 22 Operations With Sets

Lecture 23 Set Theory Relations

Section 7: Finite Automata

Lecture 24 Theory of Computation

Lecture 25 Finite Automata

Lecture 26 Deterministic Finite Automata (DFA)

Lecture 27 DFA Challenge

Section 8: Non - Deterministic Finite Automata & Regular Operations

Lecture 28 Non - Deterministic Finite Automata

Lecture 29 NFA Examples: Practical Exercise

Lecture 30 Operations With Languages

Lecture 31 Regular Languages

Lecture 32 Regular Expressions

Section 9: Numbers Theory

Lecture 33 Divisability

Lecture 34 Euclidean Algorithm

Lecture 35 Modular Arithmetic

Lecture 36 Modular Addition and Multiplication

Lecture 37 Prime Number Functions

Lecture 38 Prime Number Testing

Section 10: Cyber Security: Public Key Cryptography

Lecture 39 Encryption and Decryption of Public Keys

Lecture 40 Encryption and Decryption of Schemes

Lecture 41 Advanced RSA Algorithm

Lecture 42 Key Generation with RSA: Practical Exercise

Lecture 43 RSA Exercise Solution

Lecture 44 Key Exchange Algorithm of Diffie - Hellman

Lecture 45 Key Exchange Algorithm: Exercise Solution

Section 11: Dijkstra Algorithm

Lecture 46 Dijkstra Algorithm | Part 1

Lecture 47 Dijkstra Algorithm | Part 2

Section 12: Bonus Lecture

Lecture 48 Bonus Lecture

Beginner Java Developers,Beginner Python Developers,Beginner C & C++ Developers,Computer Science Students,Engineering Students,Employees in Programming Companies