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.
- 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
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:
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.
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.