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

Schedule

  • Class hours will be in person from 9AM – 3PM (PST)
Day 1Day 2Day 3Day 4Day 5
9 - 10:30 AMIntroduction to Python ProgrammingIterable VariablesFunctionsHashingNumpy II
10:30-11AMQ&AQ&AQ&AQ&AQ&A
11-12:30PMBasic Python VariablesConditions and LoopsClasses and OOPNumpy IScientific Visualization
12:30-1:30PMLunch BreakLunch BreakLunch BreakLunch BreakLunch Break
1:30-3PMPython Setup and ExercisesDebugging and ExercisesCoding ExercisesCoding ExercisesCoding Exercises
Day 6Day 7Day 8 Day 9 Day 10
9 - 10:30 AMGradient DescentDeep Neural NetworksIntroduction to Autonomous DrivingReinforcement LearningGenerative Pretrained Transformer (GPT) I
10:30-11AMQ&AQ&AQ&AQ&AQ&A
11-12:30PMIntroduction to Machine LearningConvolutional Neural NetworksPID Control for Lane followingReinforcement Learning IIGPT II
12:30-1:30PMLunch BreakLunch BreakLunch BreakLunch BreakLunch Break
1:30-3PMCoding ExercisesCoding ExercisesSimulation: Racing Practice Simulation: Racing PracticeMaking 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.

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Recent News

  • Applications for the Summer 2025 ROAR Academy at UC Berkeley are now open!
  • Fall 2024 Simulation Racing Series Results
  • Speeding to Tier Victory @CES ’25
  • AV-24s RETURN TO LAS VEGAS
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