Program Goals
The goal of the academy is to support you in your exploration of engineering as a career path and to provide an in-depth and interactive overview of engineering majors, programming, hardware and software design, and allow students to use this experience in college applications.
- Design innovation via hands on learning
- Learn new skills in coding, programming, hardware and software design
- Engineering Applications for the Real World
- Exploration of engineering topics
Sesssions
| Week 1 | Week 2 | |
|---|---|---|
| Session 1 | July 6 - July 10 (Sat) | July 12 - July 16 |
| Session 2 | July 19 - July 23 | July 26 - July 30 |
| Session 3 | August 2 - August 6 | August 9 - August 13 |
Schedule
- Class hours will be online from 9AM – 4PM (PST)
- Virtual Engineering and Programming class
- Live virtual hours to help with ROAR project
| Day 1 | Day 2 | Day 3 | Day 4 | Day 5 | |
|---|---|---|---|---|---|
| 9AM - 10:30AM | Introduction to Python Programming | Strings and Text Input/Output | Conditions and Loops | Turples and Dictionaries | Classes and OOP I |
| 10:30AM - 12PM | Numeric Variables | Lists | Functions | Sets and Hashing | Classes and OOP II |
| Lunch Break | Lunch Break | Lunch Break | Lunch Break | Lunch Break | Lunch Break |
| 1:30PM - 4PM | Python/Jupyter Notebook Setup | Coding Exercises | Coding Exercises | Coding Exercises | Coding Exercises |
| Day 6 | Day 7 | Day 8 | Day 9 | Day 10 | |
|---|---|---|---|---|---|
| 9AM - 10:30AM | Numpy | Vectors and Matrices | Introduction to Machine Learning | Introduction to Autonomous Driving | Introduction to Reinforcement Learning |
| 10:30AM - 12PM | Visualization | Gradient Descent | Tuning Deep Neural Networks | PID Control for Lane following | Training Controllers using Gym |
| Lunch Break | Lunch Break | Lunch Break | Lunch Break | Lunch Break | Lunch Break |
| 1:30PM - 4PM | Debugging in IDE | Using Git and GitHub | Setup Neural Simulator | ROAR S2 racing Practice | ROAR S2 racing Final |
Requirements
Access to a computer and a good internet connection are the only requirements to participate in the program. All the material will be hosted online and easily accessible from a web browser and any additional software tool will be made freely available to the students
Students will learn about:
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python’s simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
The continuing evolution of automotive technology aims to deliver even greater safety benefits and automated driving systems (ADS) that — one day — can handle the whole task of driving when we don’t want to or can’t do it ourselves. Fully automated cars and trucks that drive us, instead of us driving them, will become a reality.
– NHTSA
Reinforcement learning is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.
