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
Upcoming Course Dates
