Advanced-AI

⛓ Projects and notes from CPSC 422: Advanced AI

Contents:

• Partially-Observable-MDP.ipynb: Programatically computing belief states given actions/observations in a Partially Observable Markov Decision Process (POMDP)
• Approximate-Reasoning-Belief-Networks.ipynb: Approximate inference in belief networks using rejection sampling and likelihood weighting
• Undirected-Graphical-Models.ipynb: Implementing Gibbs Sampling for approximate inference in Markov Networks
• WalkSAT.ipynb: Exploring difficulty of satisfiability problems using WalkSAT
  • walkstat.sh: Shell script to run WalkSAT in batches and extract data from each run
• Parse-Trees-Probabilistic-Context-Free-Grammar.ipynb: Generating probabilistic context-free grammar from a corpus using Natural Language Toolkit (NLTK)

Credits to Professor Giuseppe Carenini for creating these projects, and guiding my understanding of Artificial Intelligence. Link to original course website here.