Goals and Schedule

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.

Sesssions

*July 4th (holiday) and will run into Saturday, July 8

	Week 1	Week 2
Session 1*	June 26 - June 30	July 3 - July 8
Session 2	July 10 - July 14	July 17 - July 21
Session 3	July 24 - July 28	July 31 - August 4

Schedule

Class hours will be online from 9AM – 3PM (PST)
Virtual Engineering and Programming class
Live virtual hours to help with ROAR project

	Day 1	Day 2	Day 3	Day 4	Day 5
9 - 10:30 AM	Introduction to Python Programming	Strings and Text Input/Output	Conditions and Loops	Turples and Dictionaries	Classes and OOP I
10:30-11AM	Q&A	Q&A	Q&A	Q&A	Q&A
11-12:30PM	Numeric Variables	Lists	Functions	Sets and Hashing	Classes and OOP II
12:30-1:30PM	Lunch Break	Lunch Break	Lunch Break	Lunch Break	Lunch Break
1:30-3PM	Python/Kaggle Setup	Coding Exercises	Coding Exercises	Coding Exercises	Coding Exercises

	Day 6	Day 7	Day 8	Day 9	Day 10
9 - 10:30 AM	Numpy	Vectors and Matrices	Introduction to Machine Learning	Introduction to Autonomous Driving	Introduction to Reinforcement Learning
10:30-11AM	Q&A	Q&A	Q&A	Q&A	Q&A
11-12:30PM	Visualization	Gradient Descent	Tuning Deep Neural Networks	PID Control for Lane following	Training Controllers using Gym
12:30-1:30PM	Lunch Break	Lunch Break	Lunch Break	Lunch Break	Lunch Break
1:30-3PM	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:

Introduction to Python Programming

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.

Introduction to Machine Learning

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.

Introduction to Autonomous Driving

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

Introduction to Reinforcement Learning

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.