# KodeKloud: Beta Courses Mathematics for Computing

## Mathematics for Computing

### Introduction and Overview

- [Course Introduction](https://notes.kodekloud.com/docs/Mathematics-for-Computing/Introduction-and-Overview/Course-Introduction/page.md): Introductory course on mathematics for computing covering linear algebra, calculus, optimization, backpropagation, and probability with practical examples and hands-on applications.

### Linear Algebra

- [Vectors Matrices and Tensors Part 1](https://notes.kodekloud.com/docs/Mathematics-for-Computing/Linear-Algebra/Vectors-Matrices-and-Tensors-Part-1/page.md): Explains how vectors, matrices, and tensors represent data for search, related-item discovery, and personalized recommendations in e-commerce.
- [Vectors Matrices and Tensors Part 2](https://notes.kodekloud.com/docs/Mathematics-for-Computing/Linear-Algebra/Vectors-Matrices-and-Tensors-Part-2/page.md): Explains vectors, matrices, and tensors and how multi-dimensional arrays power recommendation systems by representing products, features, users and interactions for richer predictions
- [Matrix Operations Part 1](https://notes.kodekloud.com/docs/Mathematics-for-Computing/Linear-Algebra/Matrix-Operations-Part-1/page.md): Using matrices for sensor fusion and coordinate transforms in autonomous vehicles, explaining matrix multiplication rules and examples for mapping sensor data into a vehicle frame
- [Matrix Operations Part 2](https://notes.kodekloud.com/docs/Mathematics-for-Computing/Linear-Algebra/Matrix-Operations-Part-2/page.md): Explains how diagonal and non diagonal matrices transform sensor data, scale and mix channels, and how inverses correct vehicle motion for accurate sensor fusion
- [Matrix Operations Part 3](https://notes.kodekloud.com/docs/Mathematics-for-Computing/Linear-Algebra/Matrix-Operations-Part-3/page.md): Explains how to compute and verify the inverse of a 2×2 matrix, why the determinant matters, and applications to correcting sensor data and solving linear systems.

### Calculus

- [Derivatives and Gradients Part 1](https://notes.kodekloud.com/docs/Mathematics-for-Computing/Calculus/Derivatives-and-Gradients-Part-1/page.md): Introductory lesson on derivatives explaining rate of change, tangents, instantaneous speed, practical applications in self driving and machine learning, and an introduction to the power rule.
- [Derivatives and Gradients Part 2](https://notes.kodekloud.com/docs/Mathematics-for-Computing/Calculus/Derivatives-and-Gradients-Part-2/page.md): Explains derivatives and differentiation rules including the power rule, interpreting rates of change, tangents, and applications in motion, self driving, and computer vision
- [Partial Derivatives and Gradient Descent Part 1](https://notes.kodekloud.com/docs/Mathematics-for-Computing/Calculus/Partial-Derivatives-and-Gradient-Descent-Part-1/page.md): Explains partial derivatives, gradients, and introductory gradient descent for optimizing multivariable problems such as routing and travel time.
- [Partial Derivatives and Gradient Descent Part 2](https://notes.kodekloud.com/docs/Mathematics-for-Computing/Calculus/Partial-Derivatives-and-Gradient-Descent-Part-2/page.md): Explains partial derivatives, gradients, and gradient descent applied to optimization and routing examples such as travel-time minimization.
- [Backpropagation in Neural Networks](https://notes.kodekloud.com/docs/Mathematics-for-Computing/Calculus/Backpropagation-in-Neural-Networks/page.md): Explains backpropagation, the chain rule, and gradient descent using a numeric Netflix recommendation example to compute gradients and update neural network weights.

### Probability and Statistics

- [Probability Distribution Part 1](https://notes.kodekloud.com/docs/Mathematics-for-Computing/Probability-and-Statistics/Probability-Distribution-Part-1/page.md): Introductory explanation of probability distributions using Bernoulli, binomial, and normal examples applied to weather forecasting and decision making
- [Probability Distribution Part 2](https://notes.kodekloud.com/docs/Mathematics-for-Computing/Probability-and-Statistics/Probability-Distribution-Part-2/page.md): Explains Bernoulli and binomial distributions, probability versus expected value, examples using rain forecasts, calculations, and applications in machine learning.
- [Probability Distribution Part 3](https://notes.kodekloud.com/docs/Mathematics-for-Computing/Probability-and-Statistics/Probability-Distribution-Part-3/page.md): Explains continuous probability densities and the normal distribution, covering mean, standard deviation, 68–95–99.7 rule, probability intervals, and applications like ML sampling and anomaly detection.
- [Bayes Theorem and Statistical Inference](https://notes.kodekloud.com/docs/Mathematics-for-Computing/Probability-and-Statistics/Bayes-Theorem-and-Statistical-Inference/page.md): Explains Bayes' theorem and how to update probabilities with examples like spam detection and DJ identification, highlighting applications in data science and decision making under uncertainty.