AI Foundations Roadmap

AI Foundations Roadmap

AI Foundations Roadmap

Module 1: AI Fundamentals (3-5 Days)

Core Concepts

  • Definitions: Narrow AI vs AGI vs ASI
  • History: From Turing Test to modern LLMs
  • AI Paradigms: Symbolic AI vs Machine Learning
  • Ethics: Alignment problem, bias, existential risks

Key Theories

  • Turing Machines & Church-Turing thesis
  • Moravec's Paradox ("Hard problems are easy")
  • Bitter Lesson (Sutton, 2019)

Module 2: Problem-Solving Foundations (5-7 Days)

Search & Optimization

  • State-space representation
  • Algorithms: BFS, DFS, A*, Minimax
  • Constraint satisfaction problems

Knowledge Representation

  • Propositional & First-Order Logic
  • Semantic Networks & Frames
  • Ontologies & Knowledge Graphs

Module 3: Intelligent Agents (5 Days)

Agent Architectures

  • PEAS framework (Performance, Environment, Actuators, Sensors)
  • Reactive vs Deliberative agents
  • BDI Model (Belief-Desire-Intention)

Learning Agents

  • Utility-based agents
  • Q-Learning basics (transition to ML)
  • Exploration vs Exploitation

Module 4: AI Mathematics Refresher (2-3 Days)

Must-Know Concepts

  • Graph Theory: Trees, Markov Chains
  • Information Theory: Entropy, KL Divergence
  • Game Theory: Nash Equilibrium

Implementation Prep

  • Python OOP for agent modeling
  • Basic Graph Traversal code

After Mastering These Foundations → Proceed to ML Core Roadmap

You'll be ready when:

  • You can code A* search from memory
  • Understand why AlphaGo uses MCTS + Neural Nets
  • Explain PEAS for a self-driving car agent

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