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AI for Leaders: Complete Curriculum Overview

This document provides the hierarchical structure and learning progression for the AI for Leaders curriculum. It's designed to guide leaders from foundational concepts to advanced AI strategy and implementation.

Curriculum Philosophy​

Progressive Complexity: Start with intuitive business concepts, gradually introduce technical depth without losing business focus.

Market-Driven: Every topic connects to real-world business applications and competitive advantages.

Leader-Focused: Content is structured for decision-makers, not individual contributors, emphasizing strategic implications over technical implementation.

Phase 1: Foundation (AI Fundamentals)​

1.1 Machine Learning Fundamentals​

  • Topics Covered:
    • What is Machine Learning?
    • How machines learn (business analogies)
    • The four critical ML steps
    • Training vs. simple math formulas

1.2 ML Problem Types & Learning Approaches​

  • Topics Covered:
    • Classification vs. Regression
    • Supervised vs. Unsupervised Learning
    • Key differences and business applications
    • Strategic decision-making framework

1.3 Classical ML Algorithms Overview​

  • Topics Covered:
    • Linear Regression (Trend Predictor)
    • Logistic Regression (Probability Calculator)
    • Decision Trees (Flowchart Decision Maker)
    • Random Forest (Committee of Experts)
    • Clustering (Pattern Discovery Engine)

1.4 Market Applications of Classical ML​

  • Topics Covered:
    • Linear Models in Financial Services & E-commerce
    • Tree-Based Models in Fintech & Insurance
    • Unsupervised Learning in Customer Analytics
    • Neural Networks for Capability Unlocking

1.5 Model Complexity & Tradeoffs​

  • Topics to Cover:
    • Underfitting vs. Overfitting (business analogies)
    • Model complexity vs. business value
    • Bias-variance tradeoff in strategic terms
    • When to choose simpler vs. complex models

1.6 Exploratory Data Analysis (EDA) for Leaders​

  • Topics to Cover:
    • Data quality assessment from business perspective
    • Identifying patterns and anomalies
    • Data preparation strategy
    • Business impact of data quality decisions
  • Interactive Learning: Try our EDA Web Application to explore data analysis concepts hands-on

Phase 2: Deep Learning & Modern AI (Technical Depth)​

2.1 Neural Network Fundamentals​

  • Topics to Cover:
    • Neural Network Internals (Input, Output, Forward Pass, Backward Pass)
    • Gradient Descent from business optimization perspective
    • Why neural networks unlock new capabilities
    • Business applications vs. classical ML

2.2 Computer Vision & CNNs​

  • Topics to Cover:
    • CNN Architecture (business applications)
    • Convolution blocks and feature maps
    • Forward pass and backpropagation (conceptual)
    • Interactive demos: Vision applications

2.3 Natural Language Processing & Transformers​

  • Topics to Cover:
    • GPT Tokenizer and text processing
    • Transformer Architecture
    • Pre-training vs. Post-training
    • LLM Architecture overview

2.4 Large Language Models (LLMs)​

  • Topics to Cover:
    • Understanding ChatGPT/Gemini capabilities
    • Tokenization and training process
    • Business applications and limitations
    • Cost and resource considerations

2.5 Interactive AI Demos​

  • Topics to Cover:
    • CNN Vision Demo
    • Transformer NLP Demo
    • LLM Chat Interface Demo
    • Hands-on business applications

Phase 3: AI Integration & Strategy (Business Applications)​

3.1 AI in Decision-Making​

  • Topics to Cover:
    • The evolving role of AI in strategic decisions
    • AI-powered data analysis & visualization
    • Predictive & prescriptive insights
    • Integrating AI into BI workflows

3.2 AI Agents vs. Automation​

  • Topics to Cover:
    • Agents vs. traditional automation
    • Case studies: Copilots and customer agents
    • Deploying AI in team workflows
    • Business process transformation

3.3 AI Agent Applications​

  • Topics to Cover:
    • Research Agents for knowledge work
    • Copilot models for productivity
    • Customer engagement agents
    • Cross-cutting lessons and patterns

3.4 AI Workflow Integration​

  • Topics to Cover:
    • Understanding agent deployment models
    • Engineering workflow integration
    • Product workflow optimization
    • Operations workflow automation

Phase 4: Strategic Implementation (Leadership Focus)​

4.1 AI Strategy & Governance​

  • Topics to Cover:
    • AI task forces and governance structures
    • Responsible AI and compliance (DPDP/GDPR)
    • Pilots to scaling strategies
    • Risk management and ethical considerations

4.2 Product Integration Framework​

  • Topics to Cover:
    • CUJ (Critical User Journey) framework
    • Industry examples (FinTech, SaaS, Healthcare)
    • Identifying leverage points in roadmaps
    • AI integration prioritization

4.3 AI Leverage & Competitive Advantage​

  • Topics to Cover:
    • Foundations of AI leverage
    • Spotting opportunities in product lifecycle
    • Frameworks for prioritizing AI integration
    • Case studies and benchmarks

4.4 Change Management & Deployment​

  • Topics to Cover:
    • Governance and change management
    • Team workflow transformation
    • Measuring AI impact and ROI
    • Scaling AI initiatives across organization

Learning Progression & Dependencies​

Foundation First (Phase 1)​

  • Essential for all leaders
  • Business-focused, minimal technical depth
  • Establishes common language and concepts

Technical Depth (Phase 2)​

  • For leaders in tech companies or AI-first organizations
  • Deeper understanding of modern AI capabilities
  • Interactive demos for hands-on experience

Strategic Integration (Phase 3)​

  • For leaders implementing AI in existing products/services
  • Focus on practical business applications
  • Case studies and real-world examples

Advanced Strategy (Phase 4)​

  • For senior leaders and executives
  • Organizational transformation and governance
  • Long-term AI strategy and competitive positioning

This curriculum is designed to evolve with the AI landscape. Feedback from the AI Leadership Fellowship community helps shape the content and priorities.