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Intro to Self-Driving Cars

This innovative curriculum in Australia is teaching you all the fundamentals to get you started in autonomous vehicles.

Time commitment
4 months (10 hours per week) 100% online
Next start date
20 Jan 2020
Prerequisites
Basic experience in programming and algebra. Comfortable reading and modifying code
A$ 2600 incl GST
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AUSTRALIAN INDUSTRY PARTNERS


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Why learn Intro to Self-Driving Cars

By 2020 the autonomous vehicle market will be worth AU$120 billion (Accenture) - we cannot ignore the role that autonomous vehicles will play both here in Australia and on a global stage. This is Australia’s first dedicated online short course committed to educating the next wave of future engineers. This course will teach you the fundamentals of building a self-driving car with only minimal programming experience. You will learn both Python and C++ , providing you with an exhilarating first step into this exciting world.

How does it work

This course will introduce you to all the tools vital for self-driving car engineers. You will learn how a computer sees an image and how to use machines to teach a computer to identify images programmatically. During the 4 months, you’ll get to practise how to write code for self-driving cars, and plan and visualise the trajectory for it. 

Included in the course is a site visit to Bosch Australia, where you will have the opportunity to learn from a leading industry partner about the challenges and opportunities within the ecosystem. 

Skills learned

Bayesian Thinking, C++, algorithmic thinking, object-oriented programming, linear algebra, Python's visualisation libraries for trajectory planning, machine learning and computer vision.

Find out more in our FAQ section.

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After completing an RMIT Future Skills course, you will earn an RMIT certificate and credential which can be validated, recognised and shared on social media platforms.

Course Structure

Module 1

Bayesian Thinking

Learn the framework that underlies a self-driving car's understanding of itself with basic algebra.

Module 2

Working with Matrices

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

Module 3

C ++ Basics

First step towards C++ expertise. The goal is translation: get a program written in Python and translate into C++

Module 4

Performance Programming in C++

Explore how to write good code that runs correctly. Low level features of C++ and other best practices.

Module 5

Navigating Complex Data Structures

Algorithmic thinking and frequently used data structures.

Module 6

Visualising Calculus and Controls

Basic calculus – the mathematics of continuity. Python's most popular visualisation libraries.

Module 7

Machine learning and Computer Vision

Learn how a computer sees an image and how to use machine learning to teach a computer to identify images programmatically.

Learn with Industry Experts

Get ready to meet some of the biggest names in the business

Kate CousinsLead Technical Specialist Engineer, Holden

After completing my Bachelor of Engineering in Robotics and Mechatronics, I joined Holden as a member of the Engineering Graduate Program. Now, over 12 years later I am the Lead Technical Specialist Engineer for Active Safety and Automated Driving.  GM is one of the leaders in the automated driving space, so being involved in this area is amazingly rewarding and exciting. I am involved in ensuring that these features are appropriate for Australian conditions as well being involved on global products. The work I do directly impacts GM’s vision of “zero crashes, zero emissions, zero congestion”. My job is amazing, but it is the culture and people at Holden that makes me enjoy coming to work every day. I am proud to say I work at Holden.

Nathan NguyenCommunity Manager, MTAiQ

Nathan is the Community Manager for MTAiQ, Australia’s first automotive innovation hub, which has been established by the Motor Trades Association of Queensland. A resourceful innovator and entrepreneur, Nathan has over 10 years of experience working in the automotive industry. With a background in economics, business and marketing, Nathan has been involved in developing and launching several start-up businesses and engaging with, and providing solutions to, the MTAiQ community and assisting clients.

Rodrigo Perez KlennerSolution Consultant, Kapsch

Rodrigo is highly skilled Electrical Engineer with a Master of Arts in Statistics and a strong technical knowledge gained through more than 10 years of professional experience gathering, analysing business requirements and delivering solutions while acting as the nexus between the stakeholders and the technical team. Rodrigo has participated in projects with different complexities as Architect, Team Leader, System Engineer during the design phase defining user histories, uses cases and supervising the delivery and testing phases of the project. He has a passion for innovation and understanding the mechanics of how things work.

Kelvin LwinKnowledge Architect | Senior Deep Learning Instructor, Nvidia

After spending nearly a decade at UC Berkeley, Kelvin decided to repay his debt to the public education system by helping build UC Merced. He spent seven years teaching 4,500 students across 55 classes, while redesigning the undergraduate Computer Science curriculum. He is now busy designing curricula at NVIDIA’s Deep Learning Institute (DLI) to democratize access to the latest technologies across many disciplines, industries and geographies. Kelvin helped DLI reach over 100K developers worldwide directly and in collaboration with Udacity and Coursera/Deeplearning.ai. He continues to search for ways to leverage AI to solve the Paradox of Progress.

Xavier VagedesProject Leader Automated Driving, Bosch

Xavier is a Systems Engineer with over 15 years’ experience in the development of vehicle safety technologies. His first contact with Automated Driving was in 2002, working on a driverless truck project at Daimler-Benz Research in Germany. Since working at Bosch, he has moved around the world working on the development of Chassis Control and Advanced Driver Assistance Systems. As Scrum Master, and then Project Leader for Automated Driving, he has been focusing on the challenges of system integration of complex networked vehicle systems since the start of the Bosch Automated Driving program.

Marcus BurkeDirector, Automated Vehicles, National Transport Commission

Marcus is responsible for transport technology projects, including developing the legal framework for automated vehicles. Over the last two years he has lead a team at the NTC examining the regulatory barriers to automated vehicles and developing the roadmap for future reform. He has presented at conferences both in Australia and internationally on the legal issues associated with automated vehicles and has also appeared before parliamentary inquiries at the state and federal level.  

 

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