Introduction to Artificial Intelligence PDF SLIDES

Introduction to Artificial Intelligence

Instructor: Henry Kautz
Textbook:  Artificial Intelligence: A Modern Approach (3rd Edition), Stuart Russell & Peter Norvig
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Topic	Slides
search & planning	search.ppt graph-search demo
game playing	games_and_pp.ppt
from propositional logic to Prolog	logic-to-prolog.ppt Online Prolog tutuorial: http://www.amzi.com/ AdventureInProlog/
natural language processing	nlp-all.pdf
introduction to probabilistic reasoning	(see slides below)
algorithms for probabilistic reasoning	revised & updated set of slides on probablistic reasoning: revised-rn-bayes-all.pdf
robotics & probabilistic reasoning over time	Probabilistic Reasoning Over Time- includes description of particle filtering algorithm rn-ch15-abbrev.pdf- slides for R&N Chapter 15
rule learning	Decision Trees
neural networks & Bayesian learning	Neural Nets Bayesian Learning
local search genetic algorithms	local search
take home final exam due, either by email to kautz@cs.washington.edu or by hardcopy delivered to the CSE front desk.

Application of Artificial Intelligence PDF SLIDES

Application of Artificial Intelligence

Instructor: Rajesh Rao
Textbook: Stuart Russell & Peter Norvig, Artificial Intelligence A Modern Approach, Third edition
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Topics & Lecture Notes	Readings
Introduction 2 slides/page 1 slide/page	Russell & Norvig, Chapters 1-2
Problem Solving using Search 2 slides/page 1 slide/page	Russell & Norvig, Chapters 3-5 (3,4,6 in 2nd ed)
Adversarial Search, Logic and Reasoning 2 slides/page 1 slide/page	Russell & Norvig, Chapters 8-9
Logic, Uncertainty and Probabilistic Reasoning 2 slides/page 1 slide/page	Russell & Norvig, Chapters 13-14
Invited lecture: Dieter Fox (Director, Intel Seattle Lab) Probabilistic Reasoning and Applications	Russell & Norvig, Chapters 14-15
Supervised Learning: Classification and Regression 2 slides/page 1 slide/page	Russell & Norvig, Chapter 18
Unsupervised Learning, Applications in Vision 2 slides/page 1 slide/page Applications in Robotics	Russell & Norvig, Chapters 20, 24, 25
Machine learning applications in industry	Talk Highlights and Useful Links: SnapTell Mobile Visual Search AdSense NetFlicks Competition Random Projections Random Forests CRM114 Email/Data Filtering Software
Overview: AI - Past, Present, and Future. 2 slides/page 1 slide/page	Russell & Norvig, Chapters 22, 26, 27

Applications of Artificial Intelligence PDF LECTURE SLIDES

Applications of Artificial Intelligence

Instructor: Mausam
Textbook: Stuart Russell & Peter Norvig, Artificial Intelligence: A Modern Approach, Third edition
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sl	Topics & Lecture Notes	Readings	Resources and Advanced Readings
1	Introduction, Uninformed Search, Informed Search.	AIMA Chapters 1,3 (Extra reading: Ch. 2, Beam Search)	Video Anytime A* Dynamic A*
2	Local Search, Adversarial Search, Computational Voting Theory.	AIMA 4.1-4.2, 5.1-5.4, 5.7-5.9, 6 (Extra reading: 5.5, 5.6)	How Intelligent is Deep Blue? General Game Playing
3	Constraint Satisfaction, Logic and Satisfiability.	AIMA 6, 7, 8.1-8.3 (Extra reading: Ch. 9)	Constraint Programming
4	Classical Planning, Agents, Decision Theory	AIMA 10, 2, 16.1-16.3, 16.6	FF Planner Self-driving cars
5	Markov Decision Processes, Probability Basics, Bayesian Networks	AIMA 17.1-17.4, 13, 14.1-14.4	Monte Carlo Planning
6	Bayesian Networks Approximate Inference and Learning, Intro to NLP	AIMA 14.5, 20.1-20.3	Future of Web Search, IBM Watson Deep QA
7	Guest Lecture: Applications of Modern SAT Solvers (Ashish Sabharwal, IBM Research) Hidden Markov Models, Intro to Learning,	AIMA 15.1-15.3, 18.1-18.2, 22.2 (Extra reading: 15.5, 15.4)
8	Guest Lecture: Learning to Make Music (Sumit Basu, Microsoft Research) Text Categorization using Naive Bayes	AIMA 18.3,18.6-18.8
9	Decision Trees, Neural Networks, Nearest Neighbor	AIMA 18.10-18.11
10	Ensemble Learning, Unsupervised Learning, Semi-supervised Learning, Wrap-up.		Dawn of AI