Subtotal:
0
Al & Machine Learning for Young Engineers - Programme Contents
Introduction to AI
- What is AI
- How AI work?
- Real-life examples of applications (e.g., recommendation systems, image recognition).
Introduction to Machine Learning
- What is machine learning?
- How do machines learn?
- Real-life examples of machine learning applications (e.g., recommendation systems, image recognition).
- Simple explanations of basic algorithms such as decision trees, k-nearest neighbors, and linear regression.
- Understanding how models make predictions or classifications.
- Simple explanations of basic algorithms such as decision trees, k-nearest neighbors, and linear regression.
- Understanding how models make predictions or classifications.
- What is data?
- Collecting and preparing data for machine learning.
- Exploring different types of features and their importance in machine learning.
- Splitting data into training and testing sets.
- Training models using data.
- Evaluating model performance using metrics like accuracy and error rates.
Types of Machine Learning
Algorithms and Models
Data and Features
Training and Evaluation
Fun Projects and Activities
- Building a simple chatbot or recommendation system.
- Creating a basic image classifier (e.g., cat vs. dog classifier).
- Training a model to play a simple game or solve a puzzle.
- Create a simple chatbot that can engage in conversation with users. Kids can program responses to specific questions or prompts using pre-defined dialogue trees.
- Build a virtual pet game where the pet's behavior is driven by basic AI algorithms. Kids can program the pet to respond to user interactions such as feeding, playing, or sleeping.
- Develop a basic image classifier that can recognize simple objects or animals in images. Kids can train the classifier using a dataset of labeled images and then test it on new images to see how well it performs.
- Implement an AI opponent for the game of Tic-Tac-Toe. Kids can program the AI to make intelligent moves based on the current state of the game board, allowing players to challenge themselves against a computer opponent.
- Create a simple recommendation system that suggests items (e.g., movies, books, games) based on user preferences. Kids can use basic algorithms such as collaborative filtering or content-based filtering to generate recommendations.
- Build a program that can solve a maze using AI algorithms such as depth-first search or breadth-first search. Kids can design their own mazes and challenge their solver to find the shortest path from start to finish.
- Experiment with speech recognition technology to build a basic voice-controlled application. Kids can program the application to respond to voice commands and perform simple tasks like playing music or answering questions.
- Develop a basic sentiment analysis tool that can analyze text and determine whether it expresses positive, negative, or neutral sentiment. Kids can use this tool to analyze social media posts, movie reviews, or other text-based content.
Practicals
Ethical and Social Considerations
- Discussing the impact of machine learning on society.
- Ethical considerations such as bias in data and algorithms.
- Promoting responsible use of technology and data privacy awareness.
Resources and Tools
- Introducing kid-friendly programming environments or tools for machine learning, such as Scratch or Cozmo.
- Recommending books, websites, and online tutorials for further exploration.
We’re delivering the best customer experience
A bunch of disruptive critical thinkers who are determined to make a difference in young adults in Sri Lanka.