Bayesian Thinking from Udacity

What's inside

Syllabus

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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops probability and conditional probability, which are core skills for robotics

Taught by Sebastian Thrun, who is recognized for their work in self-driving cars

Examines Bayes' Rule, which is highly relevant to self-driving car localization

Offers hands-on labs and interactive materials for practicing Bayesian probability programming

May require students to have some background knowledge in probability and programming

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Bayesian Thinking with these activities:

Practice coding basic math operations

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Coding proficiency is required for this course. Practice coding the basic math operations to ensure you have a strong foundation.

Browse courses on Probability

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Create a new Python script
Code the basic math operations (+, -, *, /)
Test your code with different inputs

Watch video tutorials on Bayesian probability

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Bayesian probability is a fundamental concept in self-driving cars. Familiarize yourself with its basics through video tutorials.

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Find and select video tutorials on Bayesian probability
Watch the tutorials and take notes

Organize and review course notes and materials

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Staying organized and reviewing course materials regularly helps reinforce your understanding and improve your retention.

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Gather and organize your notes, assignments, and other materials
Schedule regular review sessions

Eight other activities

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Tutorial on Bayesian Thinking

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Deepen understanding of Bayesian Thinking.

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Find a tutorial on Bayesian Thinking.
Watch the tutorial.
Apply the concepts of Bayesian Thinking to the course material.

Practice coding for controlling a (simulated) car

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Apply the knowledge for controlling gas and steering in code.

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Review lesson on controlling a (simulated) car with code.
Write code to control gas and steering.
Test your code in the simulator.

Solve Bayesian probability practice problems

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Solving practice problems helps reinforce your understanding of Bayesian probability. This will aid you during the course when applying these concepts to self-driving cars.

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Find a set of practice problems
Solve the problems step-by-step
Check your solutions

Peer Study Group

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Review and discuss the course material.

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Form a study group with peers.
Meet regularly to discuss the course material.
Work together on assignments and projects.

Join a study group to discuss course concepts

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Participating in a study group provides an opportunity to engage with peers, clarify concepts, and improve your understanding of the course material.

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Find or create a study group
Schedule regular meetings
Discuss course concepts and assignments

Probability Distribution Visualization

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Strengthen comprehension of probability distributions.

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Write a program to generate random data from a known probability distribution.
Create a visualization to represent the probability distribution.
Analyze the visualization to identify patterns and trends.

Develop a simulation to visualize Bayesian probability concepts

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Creating a simulation will deepen your understanding of Bayesian probability and its application in self-driving cars. It will also help you develop your programming skills.

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Design your simulation
Code the simulation in Python
Run the simulation and analyze the results

Create a blog post or article summarizing a course concept

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Explaining a concept to others helps solidify your understanding and identify areas where you need further clarification.

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Choose a concept to summarize
Research and gather information
Write and publish your blog post or article

Career center

Learners who complete Bayesian Thinking will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist applies machine learning and probability theory to massive data sets. A mastery of Bayesian Thinking, a key component of self-driving cars, is critical to the work of a Data Scientist. This course will provide a strong foundation in Bayesian thinking, which will be invaluable to your success as a Data Scientist. The course also covers Python programming, which is a valuable Data Science skill. Once you have completed this course, you will have a clear advantage over others entering the field of Data Science.

See salaries and explore the career path for Data Scientist

Robotics Engineer

Robotics Engineers design, build, and maintain robots, which are becoming increasingly powered by self-driving technology. To be successful as a Robotics Engineer, you will need an in-depth understanding of Bayesian Thinking as it relates to robotics. This course will teach you the fundamentals of Bayesian thinking and how it is used in robotics. You will also learn about probability distributions, which are used to represent uncertainty in sensor measurements and a vehicle's location. Ultimately, this course will help you build a strong foundation for a successful career as a Robotics Engineer.

See salaries and explore the career path for Robotics Engineer

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning systems. Machine learning plays a critical role in the development of self-driving cars and, as a Machine Learning Engineer, you will need to be an expert in Bayesian thinking. This course will teach you the basics of the math and programming skills needed to build intelligent machine learning systems. In this course, you will also benefit from learning how a robot represents its belief about uncertain quantities using probability distributions. Overall, this course can help you become a more competent and effective Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts interpret and visualize data to help organizations make better decisions. Bayesian thinking is a key component of data analysis, and this course will provide you with the skills you need to succeed in this role. You will learn the basics of probability and conditional probability, which are essential skills for Data Analysts. You will also learn about Bayesian statistics, which is used to make predictions and draw conclusions from data. Overall, this course will help you build a strong foundation for a successful career as an Data Analyst.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers design, develop, and maintain software systems. Bayesian thinking is used in the development of self-driving cars, and as a Software Engineer, you will benefit from a strong understanding of Bayesian Thinking. This course will teach you the basics of the math and programming skills needed to build intelligent software systems. In this course, you will also benefit from learning how to program Bayesian probability in Python, a valuable skill for Software Engineers. Overall, this course can help you become a more competent and effective Software Engineer.

See salaries and explore the career path for Software Engineer

Data Engineer

Data Engineers design and build data pipelines. Bayesian thinking is used in the development of self-driving cars, and as a Data Engineer, you will need an understanding of Bayesian Thinking. This course will teach you the basics of the math and programming skills needed to build intelligent data pipelines. In this course, you will also benefit from learning how to program Bayesian probability in Python, a valuable skill for Data Engineers. Overall, this course can help you become a more competent and effective Data Engineer.

See salaries and explore the career path for Data Engineer

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. Bayesian thinking is a key component of quantitative analysis, and this course will provide you with the skills you need to succeed in this role. You will learn the basics of probability and conditional probability, which are essential skills for Quantitative Analysts. You will also learn about Bayesian statistics, which is used to make predictions and draw conclusions from data. Overall, this course will help you build a strong foundation for a successful career as an Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst

Actuary

Actuaries use mathematical and statistical models to assess and manage risk. Bayesian thinking is a key component of actuarial science, and this course will provide you with the skills you need to succeed in this role. You will learn the basics of probability and conditional probability, which are essential skills for Actuaries. You will also learn about Bayesian statistics, which is used to make predictions and draw conclusions from data. Overall, this course will help you build a strong foundation for a successful career as an Actuary.

See salaries and explore the career path for Actuary

Research Scientist

Research Scientists conduct research to advance scientific knowledge. Bayesian thinking is used in a variety of scientific fields, including robotics, computer science, and finance. By taking this course, you will gain a deep understanding of Bayesian thinking, which will be invaluable to your success as a Research Scientist. In this course, you will also learn about probability distributions, which are used to represent uncertainty in sensor measurements and a vehicle's location. Overall, this course will help you build a strong foundation for a successful career as a Research Scientist.

See salaries and explore the career path for Research Scientist

Artificial Intelligence Engineer

Artificial Intelligence Engineers design and build artificial intelligence systems. Bayesian thinking is a key component of artificial intelligence, and this course will provide you with the skills you need to succeed in this role. You will learn the basics of probability and conditional probability, which are essential skills for Artificial Intelligence Engineers. You will also learn about Bayesian statistics, which is used to make predictions and draw conclusions from data. Overall, this course will help you build a strong foundation for a successful career as an Artificial Intelligence Engineer.

See salaries and explore the career path for Artificial Intelligence Engineer

Machine Learning Researcher

Machine Learning Researchers develop new machine learning algorithms and techniques. Bayesian thinking is a key component of machine learning, and this course will provide you with the skills you need to succeed in this role. You will learn the basics of probability and conditional probability, which are essential skills for Machine Learning Researchers. You will also learn about Bayesian statistics, which is used to make predictions and draw conclusions from data. Overall, this course will help you build a strong foundation for a successful career as an Machine Learning Researcher.

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Statistician

Statisticians collect, analyze, and interpret data to help organizations make better decisions. Bayesian thinking is a key component of statistics, and this course will provide you with the skills you need to succeed in this role. You will learn the basics of probability and conditional probability, which are essential skills for Statisticians. You will also learn about Bayesian statistics, which is used to make predictions and draw conclusions from data. Overall, this course will give you the foundational knowledge you need to build a successful career as a Statistician.

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Data Architect

Data Architects design and build data systems. Bayesian thinking is used in the development of self-driving cars, and as a Data Architect, you will need an understanding of Bayesian Thinking. This course will teach you the basics of the math and programming skills needed to build intelligent data systems. In this course, you will also benefit from learning how to program Bayesian probability in Python, a valuable skill for Data Architects. Overall, this course can help you become a more competent and effective Data Architect.

See salaries and explore the career path for Data Architect

Financial Analyst

Financial Analysts use mathematical and statistical models to analyze financial data and make investment decisions. Bayesian thinking is a key component of financial analysis, and this course will provide you with the skills you need to succeed in this role. You will learn the basics of probability and conditional probability, which are essential skills for Financial Analysts. You will also learn about Bayesian statistics, which is used to make predictions and draw conclusions from data. Overall, this course will help you build a strong foundation for a successful career as an Financial Analyst.

See salaries and explore the career path for Financial Analyst

Product Manager

Product Managers develop and manage products, and this increasingly includes digital and web-based products. Bayesian thinking is critical to the development of self-driving cars and other intelligent products. By taking this course, you will gain a deep understanding of Bayesian thinking, which will be invaluable to your success as a Product Manager. In this course, you will also learn about probability distributions, which are used to represent uncertainty in sensor measurements and a vehicle's location. Overall, this course will help you build a strong foundation for a successful career as a Product Manager.

See salaries and explore the career path for Product Manager