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.

Sessions

	Week 1	Week 2
Session 1	July 8 - July 13	July 15 - July 19
Session 2	July 22 - July 26	July 29 - Aug 2

Schedule

Class hours will be in person from 9AM – 3PM (PST)

	Day 1	Day 2	Day 3	Day 4	Day 5
9 - 10:30 AM	Introduction to Python Programming	Iterable Variables	Functions	Hashing	Numpy II
10:30-11AM	Q&A	Q&A	Q&A	Q&A	Q&A
11-12:30PM	Basic Python Variables	Conditions and Loops	Classes and OOP	Numpy I	Scientific Visualization
12:30-1:30PM	Lunch Break	Lunch Break	Lunch Break	Lunch Break	Lunch Break
1:30-3PM	Python Setup and Exercises	Debugging and Exercises	Coding Exercises	Coding Exercises	Coding Exercises

	Day 6	Day 7	Day 8	Day 9	Day 10
9 - 10:30 AM	Gradient Descent	Deep Neural Networks	Introduction to Autonomous Driving	Reinforcement Learning	Generative Pretrained Transformer (GPT) I
10:30-11AM	Q&A	Q&A	Q&A	Q&A	Q&A
11-12:30PM	Introduction to Machine Learning	Convolutional Neural Networks	PID Control for Lane following	Reinforcement Learning II	GPT II
12:30-1:30PM	Lunch Break	Lunch Break	Lunch Break	Lunch Break	Lunch Break
1:30-3PM	Coding Exercises	Coding Exercises	Simulation: Racing Practice	Simulation: Racing Practice	Making GPT your co-pilot

Requirements

Students are expected to have sufficient background on basic computer programming in the Python language. The curriculum has built in a quick refresher of Python programming at the beginning with additional exercises and debugging practice, but the course is NOT suitable for students who have never learned about Python in the past.

Students will learn about:

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.

GPT and Large Language Models

The largest AI breakthrough in 2023 is the Turing-Test passing performance of GPT and large language models. We will demystify the technologies behind GPT, introduce alternative small language models that enable students to run a quality GPT service on their own computers, and have intelligent conversations with their own learning materials of any subjects.