Teachers Curriculum - Introduction to Machine Learning
Suitable for Educators Introducing AI and Machine Learning 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 an introduction to two types of AI, Classification and Regression.
If you are starting to teach your first class in AI, we recommend checking out the introductory unit first. You can find the introductory unit here.
Get Introduced to Artificial Intelligence and Machine Learning - a new technology that is shaping our world!
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.
Below are listed a subset of the huge array of curriculums provided by AIClub. You can also explore the corresponding book for them here. Assessment key for the questions in the book chapters 5-6 is provided here.
This unit focuses 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 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. You can find more information here.
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 two types of artificial intelligence (AI)/Machine Learning algorithms, classification and regression. It also covers different metrics used to measure the performance of these algorithms.
We recommend teaching the Introduction to AI class before this one. You can find information about it here.
• Fundamentals of AI
- Introduction to AI
- Hierarchy of AI terms
- Benefits and challenges of AI
- How does an AI learn
- How AIs get built
- How Navigator is built on Cloud
- Introduction to Classification
- Introduction to Accuracy
- Adult vs Child Exercise
- Confusion Matrix
- AI in Real Life - Detecting COVID with Smart Watches
- Introduction to Regression
- Regression Metrics
- Averages Dataset
- RMSE vs MAE
- Exercise: Car Prices Dataset
- Rules vs AI
- AI in Real Life - Dynamic Pricing
• Python Concepts
- Data-types and input/output
- Loops and conditionals
- Modules - Pandas
• Python Exercises
- 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
What teachers take away
• A good understanding of AI, its types and metrics to measure it.
• Everything they need to teach AI in a classroom.