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Nanodegree key: nd113

Version: 1.0.0

Locale: en-us

Learn the essentials of building a self-driving car, including probability, C++, machine learning, and linear algebra.

Content

Part 01 : Orientation

Welcome to the Intro to Self-Driving Cars Nanodegree program! In this section you'll get a sneak peak of the classroom, meet the team, and learn about the services provided. Then you'll take a readiness assessment and check out some learning resources to help you make the most out of your experience.

Module 01: Welcome to Intro to Self-Driving Cars!
- Lesson 01: Introduction
  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!
Module 02: How to Get Help from Peers and Mentors
- Lesson 01: Welcome
  You are starting a challenging journey. Take 3 minutes to read how to get help with projects and content.
- Lesson 02: Get Help with Your Account
  What to do if you have questions about your account or general questions about the program.
Module 03: Readiness
- Lesson 01: The Carla Chronicles: Back on Track
  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!
- Lesson 02: Get Ready
  While you wait for your classroom to open, refresh your math and programming skills with these helpful resources.

Part 02 : Bayesian Thinking

Learn the framework that underlies a self-driving car’s understanding of itself and the world around it, and to see the world the way a self-driving car does.

Module 01: Bayesian Thinking
- Lesson 01: Introduction
  A brief introduction to Bayesian Thinking from Sebastian.
  - Concept 01: The Wonderland of Bayes' Law
  - Concept 02: Course Overview
- Lesson 02: Joy Ride
  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.
  
  Project Description - Joy Ride
  
  Project Rubric - Joy Ride
- Lesson 03: Probability
  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.
- Lesson 04: Conditional Probability
  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).
- Lesson 05: Programming Probability in Python
  Your chance to learn basic Python syntax while applying what you learned about probability and conditional probability in the last two lessons.
- Lesson 06: Bayes' Rule
  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.
- Lesson 07: Programming Bayes' Rule and World Representations
  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.
- Lesson 08: Probability Distributions
  Learn how a robot represents it's belief about uncertain quantities using something known as a probability distribution.
- Lesson 09: Programming Probability Distributions
  Apply what you've learned in this course by programming and visualizing probability distributions.
- Lesson 10: Gaussian 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.
- Lesson 11: Robot Localization
  Sebastian Thrun will give you an overview of the theory behind localization!
- Lesson 12: Histogram Filter in Python
  Write the sense and move functions for a 2 dimensional histogram filter in Python.

Part 03 : Working with Matrices

This course will focus on two tools which are vital to self-driving car engineers: object oriented programming and linear algebra.

Module 01: Working with Matrices
- Lesson 01: Section Overview
  An introduction to the amazing tools and algorithms you'll learn in this lesson.
  - Concept 01: Introduction to Matrices
  - Concept 02: Course Overview
- Lesson 02: Introduction to Kalman Filters
  Learn the intuition behind the Kalman Filter, a vehicle tracking algorithm and implement a one-dimensional tracker of your own.
- Lesson 03: State and Object-Oriented Programming
  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!
- Lesson 04: Matrices and Transformation of State
  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.
- Lesson 05: Implement Matrix Class
  Practice using your object oriented programming and matrix math skills by filling out the methods in a partially-completed Matrix class.
  
  Project Description - Implement a Matrix Class
  
  Project Rubric - Implement a Matrix Class

Part 04 : C++ Basics

This course is the first step in a rewarding journey towards C++ expertise. The goal is translation: get a program written in Python, and translate it into C++.

Module 01: C++ Basics
- Lesson 01: C++ Getting Started
  The differences between C++ and Python and how to write C++ code.
- Lesson 02: C++ Vectors
  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.
- Lesson 03: Practical C++
  Learn how to write C++ code on your own computer and compile it into a executable program without running into too many compilation errors.
- Lesson 04: C++ Object Oriented Programming
  Learn the syntax of C++ object oriented programming as well as some of the additional OOP features provided by the language.
- Lesson 05: Python and C++ Speed
  In this lesson, we'll compare the execution times of C++ and Python programs.
- Lesson 06: Translate Python to C++
  Apply your knowledge of C++ syntax by translating the Histogram Filter code from the first course into C++.
  
  Project Description - Translate Python to C++
  
  Project Rubric - Translate Python to C++

Part 05 : Performance Programming in C++

Explore how to write good code that runs correctly. We’ll focus primarily on low level features of C++, but we’ll discuss other best practices as well.

Module 01: Performance Programming in C++
- Lesson 01: C++ Intro to Optimization
  Optimizing C++ involves understanding how a computer actually runs your programs. You'll learn how C++ uses the CPU and RAM to execute your code and get a sense for what can slow things down.
- Lesson 02: C++ Optimization Practice
  Now you understand how C++ programs execute. It's time to learn specific optimization techniques and put them into practice. This lesson will prepare you for the lesson's code optimization project.
- Lesson 03: Project: Optimize Histogram Filter
  Get ready to optimize some C++ code. You are provided with a working 2-dimensional histogram filter; your job is to get the histogram filter code to run faster!

Part 06 : Navigating Data Structures

Algorithmic thinking is a skill you’ll refine throughout your career. In this course you’ll focus on frequently used data structures and algorithms.

Module 01: Navigating Data Structures
- Lesson 01: How to Solve Problems
  In this lesson you'll solve a hard problem with the help of Dave Evans and you'll learn a systematic approach to solving hard computer programming problems as you do.
- Lesson 02: Data Structures
  The list isn't the only structure for storing data! In this lesson you'll learn about sets, dictionaries and other Python data structures.
- Lesson 03: The Search Problem
  When programming a car to drive itself you run into problems. Many of these are "search" problems. In this lesson you'll learn what search problems are and several algorithms for solving them.
- Lesson 04: Implement Route Planner
  In this lesson you will actually implement a Google-maps style routing algorithm using A star search.
  
  Project Description - Implement Route Planner
  
  Project Rubric - Implement Route Planner

Part 07 : Vehicle Motion and Control

This course is a crash course in two branches of mathematics which are crucial to self driving cars: calculus and trigonometry. You will learn how a self driving car uses various motion sensors to help it understand its own motion.

At the end of this course you will use raw sensor data (which give information about distance driven, acceleration, and rotation rates) to reconstruct a vehicle's trajectory through space.

Module 01: Vehicle Motion and Control
- Lesson 01: Odometers, Speedometers and Derivatives
  Gain a conceptual understanding of the derivative and basic calculus by plotting points and finding slopes.
- Lesson 02: Accelerometers, Rate Gyros and Integrals
  Learn how integrals can be used to calculate accumulated changes by finding the area under a curve.
- Lesson 03: Two Dimensional Robot Motion and Trigonometry
  Learn the basics of trigonometry and how to decompose a self driving car's motion into X and Y components.
- Lesson 04: Reconstructing Trajectories from Sensor Data
  Use raw acceleration, displacement, and angular rotation data from a vehicle's accelerometer, odometer, and rate gyros to reconstruct a vehicle's X, Y trajectory.
  - Concept 01: Project Overview
  - Concept 02: Lab - Reconstructing Trajectories

Part 08 : Computer Vision and Machine Learning

In this course you’ll learn how a computer sees an image, and how we can use machine learning to teach a computer to identify images programmatically.

Module 01: Computer Vision and Machine Learning
- Lesson 01: Computer Vision and Classification
  Students will learn how to program an image classifier using computer vision techniques. Along the way you'll learn about machine learning, color transformation, feature extraction, and more!
- Lesson 02: Traffic Light Classifier
  Build a classification pipeline that takes in an image of a traffic and outputs a label that classifies the image as a: red, green, or yellow traffic light.
  
  Project Description - Traffic Light Classifier
  
  Project Rubric - Traffic Light Classifier

Part 09 : Graduation!

Congratulations! You're ready to graduate. Learn how you can continue your Udacity journey by enrolling in a Career-Ready Nanodegree Program

Module 01: Congratulations!
- Lesson 01: Congratulations! You've finished!
  Congratulations! You've reached the end of the Intro to Self-Driving Cars Nanodegree program!
  - Concept 01: Congratulations!
Module 02: Your next Nanodegree
- Lesson 01: Your next Nanodegree
  Enroll in a Career-Ready Nanodegree program
  - Concept 01: Enroll in your next Nanodegree program