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Teachers Curriculum - The Fundamentals of Artificial Intelligence

Suitable for Educators Introducing AI in Classrooms

Teach Artificial Intelligence (AI) in your classrooms effortlessly using AIClub curriculums! We provide progressive curriculums with a wide range of content and depth. They include lesson guides, videos, presentation material, exercises and assessments, as well as online support. All of the materials are available in your account online!


This comprehensive AI curriculum encompasses essential topics, including Introduction to AI, AI Ethics, Introduction to Machine Learning, and Machine Learning Algorithms. Aligned with the content of the AIClub textbook, this curriculum offers a cohesive exploration of key AI concepts and principles.

Progressive Curriculum

Below are listed a subset of the huge array of curriculums provided by AIClub. You can also explore the corresponding book for them here

Other Curriculums

Keyboard and Mouse

Introduction to AI

Teaching hours 12 hours

Prerequisites : None


AI Ethics

Teaching hours 6 hours

Prerequisites : None


Introduction to Machine Learning

Teaching hours 12 hours

Prerequisites : None

Programming Console

Machine Learning Algorithms

Teaching hours 12 hours

Prerequisites : Introduction to ML

Get Introduced to Artificial Intelligence and Machine Learning Algorithms 

Artificial Intelligence (AI) is how Google search, Alexa, Siri, auto-correct, speech translation, face recognition, self-driving cars, etc. learn from data and humans. Teach this course effortlessly using AIClub curriculums.

Why our curriculum?

• Designed by AI experts with PhDs in Computer Science!

• Only workshops where students build AIs in their first class! Students love building AIs and learning how they work!

We have no math or programming requirement. If they would like to code, they can do that also! 
Please see our brochure for more info about our programs!


Curriculum contains an introduction to artificial intelligence providing context how we are already using it in our daily life. It explains how AIs learn from data and the different sources from which data is acquired,.It also covers the topic of ethics around creation of AI in the real world and its impact on humans and environment around us. It includes introduction to different type of AI algorithms. It covers how the AI algorithms (a) K-Nearest neighbors (b) Linear Regression (c) K-Means clustering and (d) Neural Networks work internally. 


There is no math or programming pre-requisite to teach this class!

Topics Covered:

• Fundamentals of AI

  - Introduction to AI

  - Hierarchy of AI terms

  - Benefits and challenges of AI

  - AI vs Robotics

  - How is AI used in space

• How AI Learns

   - How does an AI learn

   - How AIs get built

   - Stages of AI lifecycle

   - AI and self-driving cars

   - Object detection system in a Tesla

• Data for AI

    - Sources of data

    - Data transformations

    - Statistics of data

    - Data visualization

    - Data storytelling

 • AI Ethics

    - AI Trust and Bias

    - AI and Privacy

    - Hands-on exercises

    - Discussion


    - Introduction to Classification

    - Introduction to Accuracy

    - Adult vs Child Exercise

    - Confusion Matrix

    - AI in Real Life - Detecting COVID with Smart Watches

 • Regression

    - Introduction to Regression

    - Regression Metrics

    - Averages Dataset

    - RMSE vs MAE

    - Exercise: Car Prices Dataset

    - Rules vs AI

    - AI in Real Life - Dynamic Pricing

K-Nearest Neighbors

   - Introduction to the KNN algorithm

   - Exercise - House Prices

   - Demo: KNN

   - AI in Real Life - Dynamic Pricing

Linear Regression

    - Linear regression

    - Averages Exercise

    - Recap RMSE vs MAE

    - Demo: Linear Regression

    - Product Exercise

    - Regression for Curves

    - Curve Exercise

    - Compare KNN and Linear Regression

 K-Means Clustering

    - Unsupervised learning

    - Introduction to Clustering

    - K-means clustering

    - AI in Real Life - Fraud Detection

Neural Network

    - How to think about images

    - Introduction to Deep Learning - MLP

    - Flattening images

    - Introduction to MNIST dataset

    - Train MLP with flattened images

    - Parameters of MLP

    - Exercise - Hyper-parameter tuning in MLP

    - Train directly with color images

 Python Concepts

    - Data-types and input/output

    - Loops and conditionals

    - Lists

      - Try/Catch

    - Modules - Pandas

    - Scikit-learn

 Python Exercises

    - Read tabular data

    - Read text data

    - Read audio data

    - Read image data

    - Data Visualization

    - Accuracy with binary classification

    - Accuracy with multi-class classification

    - Confusion matrix with binary classification

    - Confusion matrix with multi-class classification

    - Mean Absolute Error

    - Root Mean Square Error

    - K-nearest neighbors classification

    - K-nearest neighbors regression

    - Linear Regression

    - K-means clustering

    - Convert a color image to grayscale

    - Resize an image

    - Flatten a grayscale image

    - MNIST and MLP

What teachers take away

• A good understanding of specific AI algorithms - K-nearest neighbors, linear regression, K-means clustering, neural networks.

• Everything they need to teach AI in a classroom.

What students have accomplished after using these curriculums!