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Applications of AI and Machine Learning

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The World of Artificial Intelligence
Description

In this lesson, students are introduced to the field of Artificial Intelligence. Students explore the definition of intelligence and the different types of artificial intelligence in computers.

Objective

Students will be able to:

  • Define Artificial Intelligence
  • Explain the difference between predictive and generative AI
Description

In this lesson, students will learn more about the types of AI and dive deeper into the three most popular machine learning algorithms.

Objective

Students will be able to:

  • Describe the difference between narrow and general AI
  • Describe the general machine learning process
  • Describe the similarities and differences of supervised, unsupervised, and reinforcement learning algorithms
  • Train and test a supervised learning model
  • Experience reinforcement learning
Description

In this lesson, students will learn about different subsets of Artificial Intelligence, specifically machine learning and neural networks.

Objective

Students will be able to:

  • Understand the differences between the concepts of AI, machine learning, and neural networks
  • Explain how machine learning is different from a traditional programming
  • Explain the fundamental concepts of how a neural network works
Description

In this lesson, students discuss important ethical issues related to the development of Artificial Intelligence, and debate the necessity of Artificial Intelligence in modern society.

Objective

Students will be able to:

  • Articulate their position on ethical issues in AI.
  • Explain how datasets can be biased and the impact that they have.
Creating an Image Recognizing AI
Description

In this lesson, students are introduced to TensorFlow and the basics for creating a Neural Network in TensorFlow.

Objective

Students will be able to:

  • Access and run Python commands from the TensorFlow library in a Colab environment
  • Create and modify basic Neural Network models using TensorFlow
Description

In this lesson, students will learn about convolutional neural networks in order to create an image prediction model. Students will have the opportunity to apply these to a TensorFlow model to make predictions about images.

Objective

Students will be able to:

  • Understand what a convolutional neural network is and how it differs from other models they have already seen.
  • Create a basic image classification model using TensorFlow
  • (optional) Understand how convolutions and max pooling are created and how they simplify the model
Description

In this lesson, students will learn about what key characteristics make up a good training dataset and explore the impact of using a biased dataset on a face-recognition model.

Objective

Students will be able to:

  • Explain how a biased dataset can impact the quality of a model
  • Understand the qualities that make up a good training dataset
Using Natural Language Processing
Description

In this lesson, students will learn about tokenizing text to be used in Natural Language Processing models. They will then use that along with embedding layers to create text sentiment models.

Objective

Students will be able to:

  • Explain what tokenization is and why it is important to machine learning
  • Explain how embedding layers are used to create text-based machine learning models
Description

In this lesson, students will learn about recurrent neural networks (RNN) and apply it to create a text-generating model using unsupervised input data.

Objective

Students will be able to:

  • Explain the basics of how an RNN works and why it would be used
  • Create a text-generating model that uses unsupervised data
Final Project
Description

In this lesson, students will demonstrate their knowledge to create a final TensorFlow model. Students can choose from one of the starter projects or choose a project of their own.

Objective

Students will be able to:

  • Independently create a machine learning model by combining concepts taught in previous units.
  • Create an AI interface to interact with the machine learning model