Learning Path:

1. Building a Knowledge Base:

   • Resources:
     • Knowledge Representation and Reasoning - MIT
     • [AI: A Modern Approach by Stuart Russell and Peter Norvig (Chapter 7-8)]
   • Notes: Explain propositional logic, first-order logic, and situation calculus.

2. Theorem Proving in First Order Logic:

   • Resources:
     • First Order Logic - Stanford University
     • [AI: A Modern Approach by Stuart Russell and Peter Norvig (Chapter 9)]
   • Notes: Define and illustrate theorem proving methods.

3. Planning:

   • Resources:
     • Planning Algorithms - MIT
     • [AI: A Modern Approach by Stuart Russell and Peter Norvig (Chapter 10)]
   • Notes: Describe partial order planning and example scenarios.

4. Uncertain Knowledge and Reasoning:

   • Resources:
     • Bayesian Networks - Carnegie Mellon University
     • [AI: A Modern Approach by Stuart Russell and Peter Norvig (Chapter 13-14)]
   • Notes: Probabilities, Bayesian networks, and reasoning under uncertainty.

5. Probabilistic Reasoning over Time:

   • Resources:
     • Probabilistic Models - Coursera
     • [AI: A Modern Approach by Stuart Russell and Peter Norvig (Chapter 15-16)]
   • Notes: HMM, Kalman filters, dynamic Bayesian networks, and tracking multiple objects.

---

Link to original note: AI-Learning ResourcesAI-Learning ResourcesTo help you gain a deep understanding of Artificial Intelligence (AI), we'll create a structured learning path based on the provided syllabus. The plan includes a detailed breakdown of each unit, along with recommendations for resources such as research papers, blogs, articles, videos, and other multimedia content. Additionally, we'll suggest methods for annotating and making notes to optimize your learning experience. Unit I - Introduction to AI * Topics1 * Learning Path1 * Multimedia Content