AI Foundations

This is an introductory course intended for students who seek an overall understanding of AI concepts, including Machine Learning, Deep Learning, GPT models, and AGI. It provides a detailed overview of AI concepts, core services, security, architecture, and support.

Course Outline

Unit 1: Introduction to AI

• 1.1 Defining Artificial Intelligence
• 1.2 Brief history of AI
• 1.3 Differences between AI, Machine Learning, and Deep Learning
• 1.4 Real-world applications of AI
• 1.5 Future perspectives on AI

Unit 2: Basics of Machine Learning

• 2.1 Defining Machine Learning
• 2.2 Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
• 2.3 Machine Learning algorithms overview
• 2.4 Real-world applications of Machine Learning

Unit 3: Introduction to Deep Learning

• 3.1 Defining Deep Learning
• 3.2 Neural Networks and Deep Neural Networks
• 3.3 Convolutional Neural Networks (CNNs)
• 3.4 Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
• 3.5 Real-world applications of Deep Learning

Unit 4: GPT Models and AGI

• 4.1 What are GPT models?
• 4.2 Understanding Generative Pre-training
• 4.3 Introduction to AGI (Artificial General Intelligence)
• 4.4 Differences between Narrow AI and AGI
• 4.5 Real-world applications and future of GPT models and AGI

Unit 5: AI Technologies Overview

• 5.1 Overview of AUTO-GPT
• 5.2 Introduction to Langchain
• 5.3 Basics of Vector Databases
• 5.4 Introduction to Pandas and data manipulation
• 5.5 How these technologies fit in the AI ecosystem

Unit 6: AI Security and Ethics

• 6.1 Security concerns with AI and AGI
• 6.2 Introduction to AI Ethics: fairness, accountability, transparency
• 6.3 Ethical considerations in developing and deploying AI models
• 6.4 Case studies on AI Ethics

Unit 7: AI Architecture and Support

• 7.1 Basics of AI system architecture
• 7.2 Understanding cloud-based AI: benefits and challenges
• 7.3 Overview of AI support tools and resources
• 7.4 Importance of community in AI development and learning

Unit 8: AI Careers and Certification Pathways

• 8.1 Overview of careers in AI
• 8.2 Importance of industry-recognized certifications
• 8.3 Pathways to advanced AI learning

Intended Audience

The AI Foundations course appeals to a diverse audience including students, working professionals, entrepreneurs, and hobbyists who have some baseline technical proficiency and a common interest in learning more about artificial intelligence concepts, technologies, and applications at an introductory level. The flexible curriculum aims to cater to varying levels of AI expertise across different backgrounds.

Prerequisites

Those attending this course should meet the following:

• Curiosity to learn about how AI systems work
• Some programming experience in any language like Python, R, Java
• Familiarity with basic mathematical and statistical concepts like algebra, probability
• Basic awareness of machine learning and artificial intelligence concepts through media, etc