WA3408

Introduction to AI and ML Training

This Artificial Intelligence and Machine Learning (AI/ML) course teaches you the fundamentals of these rapidly evolving fields, including their definitions, key components, differences, types, applications, and ethical implications. You will also learn how to analyze real-world use cases and applications of AI/ML.

Course Details

Duration

1 day

Skills Gained

  • Define AI and ML and explain their key components and differences
  • Identify and discuss the types and applications of AI/ML
  • Summarize the basic concepts of machine learning
  • Analyze real-world use cases and applications of AI/ML
  • Evaluate the ethical implications of AI and develop strategies for building trust in your AI system
Course Outline
  • 1. Introduction
    • Course Objectives
    • Major Points in the History of Machine Learning and Artificial Intelligence
    • Early Foundations
    • Birth of AI and Machine Learning
    • Growth and Challenges
    • Technological Advancements
    • The Rise of Deep Learning
    • Modern Era and Generative AI
    • Notable Developments and Applications
    • Platforms and Competitions
    • "Everything is AI"
  • Fundamental Concepts of Artificial Intelligence (AI)
    • What is Artificial Intelligence (AI)?
    • Properties of AI
    • Rule-Based Systems
    • Learning Systems
    • The Scope of AI
    • Narrow AI
    • Artificial General Intelligence
    • Super AI
    • Types of AI
    • AI Capabilities
    • Reactive AI
    • Limited Memory AI
    • Theory of Mind AI
    • Self-Aware AI
    • AI vs Human Intelligence: Key Differences
    • Relationship between AI, ML, DL, and Data Science
  • Introduction to AI and Machine Learning (ML)
    • What is Machine Learning (ML)?
    • ML Performance
    • How are AI and ML connected?
    • Deep Learning (DL)
    • Natural Language Processing (NLP)
    • Computer Vision (CV)
    • Large Language Models (LLMs)
    • Basic Concepts in ML
    • ML Model Training Process
    • Algorithm
    • Model
    • Training data
    • Test data
    • Dataset
    • Instance
    • Features
    • Target
    • Prediction
    • Types of Machine Learning
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning
    • ML is Data Driven
    • An example
    • The goal
    • Examples of ML Models
    • Regression Model
    • Binary Classification
    • Multi-class Classification Model
    • Machine Learning Training Process
    • Artificial Neurons and Neural Networks
  • Real-world Use Cases and Applications of AI and ML
    • Opening Your Phone with Facial Recognition
    • Social media
    • Preventing spam in your email
    • Smart Devices
    • Healthcare - Predictive Analytics
    • Healthcare - Monitoring and Early Warning
    • Healthcare - Drug Discovery and Manufacturing
    • Healthcare - Medical Imaging
    • Healthcare - Diagnostic AI
    • Transportation
    • Traffic Management and Analysis:
    • Route Optimization
    • Freight and Logistics Management
    • Dynamic Pricing
    • Predictive Maintenance
    • Finance - Risk Assessment and Management
    • Finance - Fraud Detection
    • Finance - Algorithmic Trading
    • Finance - Personalized Banking and Financial Advice
    • Entertainment and Media
    • Content Creation and Personalization
    • Consumer Experience Enhancement
    • Audience Analysis and Marketing
    • Gaming
    • Agriculture
    • Environment
    • Use Case: Alibaba
    • Use Case: Uber
    • Use Case: Future Autopilot
  • Trustworthy AI and Ethics
    • Ethical and Societal Considerations
    • Trust in AI Systems
    • Principles for Trustworthy AI
    • Transparency
    • Fairness
    • Security
    • Accountability
    • Real-world Issues and Controversies
    • Bias in AI
    • Job Displacement
    • Surveillance and Privacy
    • Strategies for Building Trust in AI Systems
    • Data Quality and Integrity
    • Clear Communication with Users
    • Inclusive Design and Testing
    • Role of Regulation and Governance
    • Explainable AI (XAI)
    • Interpretable Models
  • Preparing for an AI-Driven Future
    • Understanding AI’s Potential Impact on Society
    • Building AI Awareness in Various Fields
    • Embracing Continuous Learning and Adaptation
  • Key Takeaways
    • Trustworthy AI and Ethics
    • Preparing for an AI-Driven Future