Introduction to Self-Driving Cars

Welcome to the Intro to Self-Driving Cars Nanodegree program! We are excited to have you and hope you are looking forward to learning about this game-changing field!

What to do if you have questions about your account or general questions about the program.

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Work through the readiness assessment with Carla and her friends to make sure you are ready to begin your own personal adventure with self-driving cars!

While you wait for your classroom to open, refresh your math and programming skills with these helpful resources.

A brief introduction to Bayesian Thinking from Sebastian.

A quick introduction to controlling a (simulated) car with code. Parts 1 and 2 will show you how to control gas and steering and in part 3 you'll program a car to parallel park.

Learn the basics of probability - the language of robotics. This lesson will focus on the math. In later lessons you'll apply this math in Python code.

In order to infer meaning from noisy sensor measurements, a self driving car needs to use the math of Conditional Probability. Learn this math from Sebastian (and then apply it in the next lesson).

Your chance to learn basic Python syntax while applying what you learned about probability and conditional probability in the last two lessons.

Learn about Bayes' Rule from Sebastian and get your first peek at how a self driving car uses Bayes' Rule to understand where in the world it is.

In this lesson, you can expect a lot of hands-on practice programming Bayesian probability in Python, and representing a 2D world that you'll need to localize a car.

Learn how a robot represents it's belief about uncertain quantities using something known as a **probability distribution**.

Apply what you've learned in this course by programming and visualizing probability distributions.

You will work with a specific continuous probability distribution called the Gaussian distribution. A Gaussian distribution helps describe uncertainty in sensor measurements and a vehicle's location.

Sebastian Thrun will give you an overview of the theory behind localization!

Write the `sense` and `move` functions for a 2 dimensional histogram filter in Python.

An introduction to the amazing tools and algorithms you'll learn in this lesson.

Learn the intuition behind the Kalman Filter, a vehicle tracking algorithm and implement a one-dimensional tracker of your own.

In this lesson, students will learn about representing the state of a car in programming as classes and objects and mathematically as vectors that can be changed with linear algebra!

Linear Algebra is a rich branch of math and a useful tool. In this lesson you'll learn about the matrix operations that underly multidimensional Kalman Filters.

Practice using your object oriented programming and matrix math skills by filling out the methods in a partially-completed `Matrix` class.

The differences between C++ and Python and how to write C++ code.

To program matrix algebra operations and translate your Python code, you will need to use C++ Vectors. These vectors are similar to Python lists, but the syntax can be somewhat tricky.

Learn how to write C++ code on your own computer and compile it into a executable program without running into too many compilation errors.

Learn the syntax of C++ object oriented programming as well as some of the additional OOP features provided by the language.

In this lesson, we'll compare the execution times of C++ and Python programs.

Apply your knowledge of C++ syntax by translating the Histogram Filter code from the first course into C++.