About this page

This is a collection of notes and write-ups based on my lectures in AI at Northeastern. I hope that this acts as a resource for students and faculty alike!
Please note that this is very much a work in progress, and will be updated periodically. Your feedback is most welcome and appreciated.

Contributions from: Nihira Golasangi, Hasnain Sikora, and Shrey Desai, Northeastern University

Intro to AI History, food for thought The vocabulary of AI Problem formulation Search Interpreting Problems as Search Depth First Search Breadth First Search Iterative Deepening Uniform Cost Search

Informed Search Heuristics Admissibility & Consistency Greedy Best First Search A* Search Beam Search Local Search & Optimization Hill Climbing Gradient Descent Local Optima Simulated Annealing Local Beam Search Genetic Algorithms

Games & Adversarial Search Minimax Alpha-Beta Pruning Constraint Satisfaction Problems Expectiminimax Probabilistic Reasoning Markov Models Hidden Markov Models Markov Decision Processes Partially Observable MDPs Reinforcement Learning

Machine Learning Feature Extraction Naive Bayes Classification Linear Classifiers Support Vector Machines Logistic Regression Neural Networks Model Training