Mathematical Foundation for AI and Machine Learning

Mathematical Foundation for AI and Machine Learning
Mathematical Foundation for AI and Machine Learning
English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 4h 15m | 1.81 GB

Learn the core mathematical concepts for machine learning and learn to implement them in R and Python

Artificial Intelligence has gained importance in the last decade with a lot depending on the development and integration of AI in our daily lives. The progress that AI has already made is astounding with innovations like self-driving cars, medical diagnosis and even beating humans at strategy games like Go and Chess. The future for AI is extremely promising and it isn’t far from when we have our own robotic companions. This has pushed a lot of developers to start writing codes and start developing for AI and ML programs. However, learning to write algorithms for AI and ML isn’t easy and requires extensive programming and mathematical knowledge. Mathematics plays an important role as it builds the foundation for programming for these two streams. And in this course, we’ve covered exactly that. We designed a complete course to help you master the mathematical foundation required for writing programs and algorithms for AI and ML.

The course has been designed in collaboration with industry experts to help you breakdown the difficult mathematical concepts known to man into easier to understand concepts.The course covers three main mathematical theories: Linear Algebra, Multivariate Calculus and Probability Theory.

What You Will Learn

  • Refresh the mathematical concepts for AI and Machine Learning
  • Learn to implement algorithms in Python
  • Understand the how the concepts extend for real-world ML problems
Table of Contents

Introduction
1 Introduction

Linear Algebra
2 Scalars, Vectors, Matrices, and Tensors
3 Vector and Matrix Norms
4 Vectors, Matrices, and Tensors in Python
5 Special Matrices and Vectors
6 Eigenvalues and Eigenvectors
7 Norms and Eigendecomposition

Multivariate Calculus
8 Introduction to Derivatives
9 Basics of Integration
10 Gradients
11 Gradient Visualization
12 Optimization

Probability Theory
13 Intro to Probability Theory
14 Probability Distributions
15 Expectation, Variance, and Covariance
16 Graphing Probability Distributions in R
17 Covariance Matrices in R

Probability Theory
18 Special Random Variables