Grade 12 Artificial Intelligence

PREREQUISITE: Grade 12 Computer Science

GRADE: 12 (University)

In Grade 12 Artificial Intelligence, students will look at AI from both a practical and philosophical standpoint. Students will learn some introductory computer programming skills using Python, a commonly used computer programming language, as well as look at the nature of AI and its implications as it moves forward and changes our society. In Grade 12 Artificial Intelligence, students will learn how to use industry-level tools to train their own AI and solve real-life problems.

UNIT ONE

The Nature of Artificial Intelligence

Essential Question: What is artificial intelligence? Have humans created real AI? How can we test possible AI to decide?

  • In this unit, students will research the history of Artificial Intelligence. They will analyze early criteria for testing for Artificial Intelligence as well as criticism of the criteria, and other arguments about how we define AI. They will also complete lessons, readings, discussions a quiz and an assignment.

UNIT TWO

Review of Fundamental Skills

Essential Question: What are key programming skills needed to explore artificial intelligence?

  • In this unit, students will review programming basics in Python. They will be able to explore and fully understand the statement: “Python is simple, powerful and versatile programming language used in web development, data analysis, artificial intelligence, and much more”.

UNIT THREE

Linear Regression

Essential Question: How can we use linear regression to create machine learning?

  • In this unit, students will study and implement linear regression and related topics, such as hypotheses, cost functions and gradient descent. Students will analyze large datasets and begin to gain insights into the relationships within the data. Students will learn how to optimize the learning rate of their models to improve their accuracy. Students will automate the discovery of trends in multi-dimensional data.

UNIT FOUR

Logistic Regression

Essential Question: How can we use logistic regression to create machine learning?

  • In this unit, students will study and implement logistic regression, a more advanced machine learning model that can comprehend more complex relationships within data. Students will cover the new features of hypotheses, the cost function and gradient descent. Students will also learn how to prepare data for efficient, accurate machine learning. Students will engage in problem-solving to optimize the accuracy of their models using feature normalization and regularization. Students will create a binary classifier to predictively sort data into different categories.

UNIT FIVE

Artificial Neural Networks

Essential Question: How can we create artificial neural networks?

  • In this unit, students will study the fundamentals of neural networks, the technology powering modern AI, and customize a pre-built neural network to classify data with greater accuracy. Students will learn how to modify a neural network to classify different types of data for different purposes (i.e. recognizing cats in photos vs. predicting diabetes from patient records). Students will practice industry-standard machine learning development methods, including data pipelines, data collection and cleaning, and model validation.

UNIT SIX

Implications of Artificial Intelligence

Essential Question: How could artificial intelligence impact our future?

  • In this unit, students will critically evaluate the effects of artificial intelligence in the modern age. Students will synthesize different viewpoints about the ethical implications of artificial intelligence in business, politics, and law, and persuasively communicate their own perspective. Students will creatively explore the potential applications and changing limitations of artificial intelligence, and compare their current opinions to the conclusions they made in the first unit of the course.

CULMINATING PROJECT

30% of Final Grade

  • This project is the final evaluation of this course. This project will challenge students to use all concepts learned throughout this course and is worth 30% of the final grade.