Teachers Curriculum - Introduction to Machine Learning Algorithms

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 and depth. They include lesson guides, videos, presentation material, exercises and assessments, as well as online support. All the material is available online in your account!

This unit provides a gentle introduction to AI algorithms (a) K-Nearest neighbors (b) Linear Regression (c) K-Means clustering and (d) Neural Networks. We recommend teaching the introductory units before teaching this unit in your classroom (listed below).

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!

Description 

Curriculum contains an introduction to artificial intelligence providing context how we are already using it in our daily life. It also 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.

 

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

Topics Covered:
 

Recap of the fundamentals of AI

  - Introduction to AI

  - Hierarchy of AI terms

  - Benefits and challenges of AI

  - How does an AI learn?

  - Recap Regression vs Classification

  - How Navigator is built on Cloud

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 too 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

    - Modules - Pandas

    - Scikit-learn

 Python Exercises

    - 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!

Progressive Curriculum

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This unit provides a gentle introduction to AI and can be taught by educators with varying backgrounds! It covers how AI learns, data, statistics and ethics. No math or programming pre-requisites. You can find more information here.

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This unit provides a gentle introduction to two types of AI algorithms, classification and regression. It covers the metrics used to measure their performance and has several hands-on exercises. You can find more information here.

This unit provides a gentle introduction to AI algorithms: K-Nearest neighbors, Linear Regression, K-means clustering, neural networks. It contains several hands on exercises and activities. 

Below are listed a subset of the huge array of curriculums provided by AIClub. You can also explore the corresponding book for them hereAssessment key for the questions in the book chapters 7-10 is provided here.​

This unit focusses on AI Ethics and is a subset of the curriculum provided in Introduction to AI. It can be taught by educators with varying backgrounds! You can find more information about it here.

Purchase

Purchasing this curriculum will give you access to all the materials such as slides, videos, teachers guides, discussions, assessments etc needed to teach this course in your classroom.

 
 
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Supriya Bhuwalka, Educator India

Working with AIClub’s educator program to launch my own AI Classes was a great experience. The material energizes students: they are excited to learn more and want to build real world solutions using AI. The AIClub team was very helpful throughout the pilot process. I am expanding my education practice and launching AI classes with the help of AIClub!

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Education Programs,

Mission Bit

The AIClub workshop was a great introduction to artificial intelligence. I could see that the students understood the current real world applications of it and were excited about developments in the future.

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Instructor, Silicon Valley Continuing Technical Education

My 11th and 12th grade students at Silicon Valley Career Technical Education enjoyed learning AI from AIClub's High School Programs. It was a valuable experience to help them prepare for future careers. The AIClub's High School curriculum has the depth to engage students as well as an easy to use web interface. It is a Program that any Computer Science or STEAM teacher could use to teach AI.