Overview
They'll learn to solve real-world problems with Python and develop critical thinking skills. They'll also get hands-on experience with robotics by exploring electric circuits and controlling the RICE mini ai robot. Finally, they'll dive into the world of AI, creating voice-controlled systems and understanding how machines learn to understand our world. This course is perfect for sparking curiosity and building a strong foundation in today's essential tech skills.
Course Content
Knowledge points
Python
Data Structures
Control Flows
Functional Programming
Debugging
Robotics Design
Computer-Aided Design
Sensor Integration
AI Literacy
Machine Learning
Natural Language Processing
LLM Prompt Engineering
Retrieval-Augmented Generation
9-16
Tsuen Wan West
16 Weeks
16 Classes
75 mins per lesson
English
Every Sat, Sun
$23800
Please bring along personal iPad or laptop with keyboard.
Modules
Module 1
Introduction to Python Programming
This course begins with the basics of Python programming using the Google Colab platform. Students will explore fundamental concepts such as inputs and outputs, variables, functions, conditional statements, and control flows. Through hands-on exercises involving real-world STEM problems, students will build a solid coding foundation and develop critical thinking skills.
Module 2
Python Programming for Robotics
In this module, students are introduced to the field of robotics, covering topics like hardware engineering, CAD, and electric circuits. They will learn to import and utilize Python libraries to control the locomotion of the RICE mini ai. The module includes practical tasks such as integrating sensors for obstacle avoidance, allowing the robot to navigate its environment. Students will also create custom functions to enhance the robot's capabilities for specific tasks.
Module 3
Artificial Intelligence for Robotics
This module begins with an introduction to artificial intelligence (AI) and machine learning (ML), providing hands-on experience in training image and speech recognition models. Students will then use speech recognition libraries to implement voice control for the RICE mini ai. The module also covers Python data structures to manage and create custom commands, improving the robot's responsiveness and adaptability to user needs.
Module 4
Natural Language Processing
In this module, students delve into Natural Language Processing (NLP) techniques for analyzing and interpreting text. They will explore large language models (LLMs) and prompt engineering, while discussing the ethical and environmental impacts of AI. Students will also learn to customize LLMs using Retrieval-Augmented Generation (RAG) to enhance accuracy and ensure effective interactions in real-world scenarios.
The course culminates with students presenting their acquired knowledge and the robots they developed, along with reflections on their learning journey.
Schedule
