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About the course

• 1

  AI Theory and Application

  • Introduction Free Preview
  • Class syllabus Free Preview
• 2

  Class 1. What is AI? What can it do?

  • Introduction Free Preview
  • 1.1 Describe Artificial Intelligence Free Preview
  • 1.2 Examples of AI Tasks
  • 1.3 AI in Student Life
  • 1.4 Generative AI and Large Language Models
  • 1.5 AI Risks and Limitations
  • 1.6 Fixed-Rule Automation, Machine Learning, and Generative AI
  • MC Questions
  • Assignment
• 3

  Class 2. Machine Learning vs Traditional Programming, and ML Workflow

  • Introduction Free Preview
  • 2.1 Define Machine Learning Free Preview
  • 2.2 Machine Learning vs Traditional Programming
  • 2.3 The Machine Learning Workflow
  • 2.4 Data Quality, Bias, and Representation
  • 2.5 Training, Testing, Overfitting, and Evaluation
  • 2.6 Confusion Matrix, Precision, Recall, and Error Trade-Offs
  • 2.7 Worked Case Study: Applying the ML Workflow to a Real Problem
  • 2.8 Classification, Regression, and Clustering in the ML Workflow
  • Summary
  • MC Questions
• 4

  Class 3. Supervised Learning: k-Nearest Neighbours, k-NN

  • Introduction Free Preview
  • 3.1 Supervised Learning Free Preview
  • 3.2 k-Nearest Neighbours Concept
  • 3.3 Distance and Feature Scaling
  • 3.4 Choosing kin in k-NN
  • 3.5 Worked Example: Manual k-NN Classificationn
  • 3.6 Implementing a Simple k-NN Classifier
  • 3.7 From k-NN to Neural Networks and Deep Learning
  • 3.8 Limitations and Suitable Uses of k-NN
  • Assignment: Implementing and Evaluating k-NN
  • MC Questions
• 5

  Class 4: Unsupervised Learning: k-means

  • Introduction Free Preview
  • 4.1 Unsupervised Learning Free Preview
  • 4.2 k-means Clustering Concept
  • 4.3 Worked Example: One Manual Iteration of k-means
  • 4.4 Comparing k-NN and k-means
  • 4.5 Choosing k, Initial Centroids, and Limitations
  • 4.6 Implementing Simple k-means
  • Assignment
  • MC Questions
• 6

  Class 5. ML Mini-project: Formulate and Solve a Problem End-to-End

  • Introduction Free Preview
  • 5.1 Formulating an ML Problem Free Preview
  • 5.2 Preparing and Evaluating Correctly
  • 5.3 End-to-End ML Lifecycle
  • 5.4 Deployment, Monitoring, and Human-in-the-Loop Systems
  • 5.5 Responsible ML: Privacy, Fairness, and LimitationsNew Lesson
  • 5.6 Responsible AI in School and Society
  • 5.7 Using AI Responsibly in AI Project: Prompting and Verification
  • Assignment
  • MC Questions
  • Summary
• 7

  Class 6. AI Applications in Enterprises, Government, Personal Life

  • Introduction Free Preview
  • 6.1 Applications of Artificial Intelligence (6.1.1 Enterprise Applications of AI) Free Preview
  • 6.1.2 Government Applications of AI
  • 6.1.3 Personal-Life Applications of AI
  • 6.2 Applications of AI Hardware (6.2.1 Enterprise Hardware Applications)
  • 6.2.2 Government Hardware Applications
  • 6.2.3 Personal-Life Hardware Applications
  • 6.3 Trends and Challenges
  • 6.4 AI Hardware Industry Chain
  • 6.5 AI Software Industry Chain
  • Assignment
  • MC Questions
  • Summary