Lesson List
Section 1. Course Intro – Activity 1: FaceBoxes – Basic Face Detection
In this brief section, including Activity 1 we will discuss the various topics we will cover in this course, as well as some background on the concept of artificial intelligence vs natural intelligence.
0/4
Section 2. History of Computing & AI – Activity 2: What is AI?
In this section, we will discuss the early and recent history in AI technology development. We will also cover some background on semiconductors, CPU's, GPU's and other technology that make AI possible. We will also discuss the basics of AI model training, and go through some of today's most popular AI technologies.
0/1
Section 3. How is AI used in different applications? – Activity 3: Aligning real world AI with AI4K12
In this section, we will discuss several real world examples of AI robotics, and go through the exercise of aligning AI concepts with the AI4K12 5 Big Ideas in AI.
0/1
Section 4. AI in action: Teachable Machine – Activity 4: Training and testing an AI model
In this section, we will conduct a browser based activity, and will go through all the steps of developing training data, uploading it to a Google server, and training a real AI model, for object detection. We will then test our resulting model with different objects and record our results, to determine if the model is well-built and if there are any potential drawbacks or ways to improve the system.
0/2
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Lesson: Vocabulary used
Overview
Exercise Files
About Lesson

Vocabulary Terms Used:

  • Data/Dataset, n. information, organized set of information
  • Algorithm, n. a (usually math-based) computer process created to solve a specific problem
  • Prediction, a guess as to an event or an outcome that will possibly happen in the future
  • Neural network, n. a neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. Convolutional and Recurrent neural networks are key examples (CNN, and RNN, respectively)
  • Visualization, a means of showing data to make it easier to understand, such as a graph or chart
  • Sequential, data points that follow a sequence, coming one after another, such as one temperature reading after another in a thermal sensor
  • Training, exposing an AI model to many different pieces of data, in order to teach the AI model to understand that data, and be able to make better predictions or classifications regarding similar new data it is exposed to in the future
  • Model, a compilation of processed data in an AI system, that can be used to make inferences about new, unknown data
  • Inference, these are the results of an AI system, like a neural network; it is the end output that the AI system generates
Exercise Files
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Lesson List
Section 1. Course Intro – Activity 1: FaceBoxes – Basic Face Detection
In this brief section, including Activity 1 we will discuss the various topics we will cover in this course, as well as some background on the concept of artificial intelligence vs natural intelligence.
0/4
Section 2. History of Computing & AI – Activity 2: What is AI?
In this section, we will discuss the early and recent history in AI technology development. We will also cover some background on semiconductors, CPU's, GPU's and other technology that make AI possible. We will also discuss the basics of AI model training, and go through some of today's most popular AI technologies.
0/1
Section 3. How is AI used in different applications? – Activity 3: Aligning real world AI with AI4K12
In this section, we will discuss several real world examples of AI robotics, and go through the exercise of aligning AI concepts with the AI4K12 5 Big Ideas in AI.
0/1
Section 4. AI in action: Teachable Machine – Activity 4: Training and testing an AI model
In this section, we will conduct a browser based activity, and will go through all the steps of developing training data, uploading it to a Google server, and training a real AI model, for object detection. We will then test our resulting model with different objects and record our results, to determine if the model is well-built and if there are any potential drawbacks or ways to improve the system.
0/2
This feature has been disabled by the administrator
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