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
- Introduction
- Welcome to Introduction to AI and ML!
- Course Goals
- Course Format
- Course Outline
- Foundations of AI
- What is Intelligence?
- Mechanisms of Intelligence
- What is Artificial Intelligence?
- How does AI work?
- What can AI do?
- Early Foundations
- Growth and Challenges
- The Rise of Deep Learning
- Traditional AI
- Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Deep Learning: Neural Networks
- Neural Network Characteristics
- Predictive AI
- Emergence of Generative AI
- Key Approaches to Generative AI
- Two Approaches
- AI System Basics
- What is an AI system?
- How are AI Systems Created?
- Example: House Price Prediction System
- Data Collection
- Common Types of Data
- Data Processing
- Example: Tax Preparation
- Model Training
- Example: Loss Functions
- Model Evaluation
- Model Deployment
- Model Monitoring
- Real-world Use Cases of AI/ML
- Where will AI be used?
- Personalized Learning
- Fraud Detection
- Medical Diagnosis
- Customer Service
- Predictive Maintenance
- Autonomous Vehicles
- Crop Monitoring
- Climate Modeling
- Personalized Recommendations
- Drug Discovery
- Supply Chain Optimization
- Government Administration
- Energy Management
- Risk Assessment
- Financial Trading
- Gaming
- Human Resources Management
- Smart Home Devices
- Understanding Generative AI
- What is Generative AI?
- How does an Large Language Model work?
- Tokenization
- Example Tokenization: GPT-4o
- Embeddings
- What are embeddings doing?
- Why are embeddings important?
- Predicting the Next Token
- LLM Settings
- How are LLMs trained?
- How do multi-modal models work?
- What does a full Generative AI architecture look like?
- Fundamentals of AI Ethics
- What are ethics?
- The Ethical Progression
- The Point of AI Ethics
- NIST AI Risk Management Framework
- NIST AI RMF Characteristics of Trust
- NIST AI RMF Characteristics of Trust
- NIST Generative AI Risks
- State of Regulation in the US
- Blueprint for an AI Bill of Rights
- European Union AI Risk Categories
- European Union AI Risk Category Examples
