Artificial Intelligence

Unit 1: Fundamentals of AI

Unit 2: Hierarchical Control Architecture

Unit 4: Features and Constraints (CSP and Backtracking)

Unit 5: Propositions and Inference

Unit 6: Uncertainty (Bayesian Net - Markov Model)

Unit 7: Markov Decision Processes

Unit 8: Multi-Agent

Topics

Resources

UBC Course and Materials

UBC: Introduction to Artificial Intelligence with Alan Mackworth | CosmoLearning.org Computer Science

Official course page at: http://www.cs.ubc.ca/~mack/CS322/ Undergraduate level computer science course by Dr. Alan Mackworth, given at the University of Brit...

UBC: Introduction to Artificial Intelligence with Alan Mackworth | CosmoLearning.org Computer Science

https://www.youtube.com/playlist?list=PLaLOVNqqD-2EhIma70MTjfbBColi-cBPl

UBC: Introduction to Artificial Intelligence with Alan Mackworth | CosmoLearning.org Computer Science

Artificial Intelligence 2e

The full text is now freely available with stable links. You can buy a copy (and instructors can request an examination copy) from Cambridge University Press or your local bookstore or Amazon. Artificial Intelligence: Foundations of Computational Agents, second edition, Cambridge University Press 2017, is a book about the science of artificial intelligence (AI).

https://artint.info/2e/index.html

MIT Courses

Artificial Intelligence | Electrical Engineering and Computer Science | MIT OpenCourseWare

This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.

Artificial Intelligence | Electrical Engineering and Computer Science | MIT OpenCourseWare

https://ocw.mit.edu/courses/6-034-artificial-intelligence-fall-2010/

Artificial Intelligence | Electrical Engineering and Computer Science | MIT OpenCourseWare

Mathematics of Big Data and Machine Learning | Supplemental Resources | MIT OpenCourseWare

This course introduces the Dynamic Distributed Dimensional Data Model (D4M), a breakthrough in computer programming that combines graph theory, linear algebra, and databases to address problems associated with Big Data. Search, social media, ad placement, mapping, tracking, spam filtering, fraud detection, wireless communication, drug discovery, and bioinformatics all attempt to find items of interest in vast quantities of data.

Mathematics of Big Data and Machine Learning | Supplemental Resources | MIT OpenCourseWare

https://ocw.mit.edu/courses/res-ll-005-mathematics-of-big-data-and-machine-learning-january-iap-2020/

Mathematics of Big Data and Machine Learning | Supplemental Resources | MIT OpenCourseWare

Stanford

Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2021

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai

Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2021

https://www.youtube.com/playlist?list=PLoROMvodv4rOca_Ovz1DvdtWuz8BfSWL2

Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2021

CS221: Artificial Intelligence: Principles and Techniques

Course structure: To ensure accessibility, CS221 will be offered as a remote course in Autumn 2021. Below is an overview of the course components: Modules (videos and slides): All lecture materials will be delivered through modules, pre-recorded course videos that students can watch at their own time.

https://stanford-cs221.github.io/autumn2021

CS221: Artificial Intelligence: Principles and Techniques

Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019

For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019

https://www.youtube.com/playlist?list=PLoROMvodv4rO1NB9TD4iUZ3qghGEGtqNX

Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019

Causal Inference from a Machine Learning Perspective

Introduction to Causal Inference

You've found the online causal inference course page. Although, the course text is written from a machine learning perspective, this course is meant to be for anyone with the necessary prerequisites who is interested in learning the basics of causality.

Introduction to Causal Inference

https://www.bradyneal.com/causal-inference-course

Introduction to Causal Inference

www.bradyneal.com

https://www.bradyneal.com/Introduction_to_Causal_Inference-Dec17_2020-Neal.pdf

Full Lectures - Causal Inference Course

Playlist of the causal inference course lectures, where each video is a full lecture, rather than each video as a chunk of a lecture like in the other playlist.

Full Lectures - Causal Inference Course

https://www.youtube.com/playlist?list=PLoazKTcS0RzZ1SUgeOgc6SWt51gfT80N0

Full Lectures - Causal Inference Course

Causal Inference Course Lectures

Playlist of the causal inference course lectures, where each video is a conceptual chunk of a lecture.

Causal Inference Course Lectures

https://www.youtube.com/playlist?list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0

Causal Inference Course Lectures

CS50 AI

CS50's Introduction to Artificial Intelligence with Python

This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation.

CS50's Introduction to Artificial Intelligence with Python

https://cs50.harvard.edu/ai/2020/

CS50's Introduction to Artificial Intelligence with Python

CS50's Introduction to Artificial Intelligence with Python

There is one session available: AI is transforming how we live, work, and play. By enabling new technologies like self-driving cars and recommendation systems or improving old ones like medical diagnostics and search engines, the demand for expertise in AI and machine learning is growing rapidly.

CS50's Introduction to Artificial Intelligence with Python

https://www.edx.org/course/cs50s-introduction-to-artificial-intelligence-with-python

CS50's Introduction to Artificial Intelligence with Python

Other

Dana Vrajitoru Office Hours Final: Thursday, May 1. Other Links Python documentation Quick reference for the elisp Emacs interface Spring 2008 schedule: TR 4pm-5:15pm, NS#205.Prerequisite: C251. C311 recommended. Textbook: S. Russell, P. Norvig (2003): Artificial Intelligence . A Modern Approach. Second edition, Prentice Hall. Used as a reference.

https://www.cs.iusb.edu/~danav/teach/c463/

Computer Science W4701 Artificial Intelligence

Computer Science W4701 Artificial Intelligence

https://www.cs.columbia.edu/~jvoris/AI/

Artificial Intelligence: A Modern Approach, 4th US ed.

The authoritative, most-used AI textbook, adopted by over 1500 schools.Table of Contents for the US Edition (or see the Global Edition) Preface (pdf); Contents with subsections I Artificial Intelligence 1 Introduction ... 1 2 Intelligent Agents ... 36 II Problem-solving 3 Solving Problems by Searching ... 63 4 Search in Complex Environments ...

Artificial Intelligence: A Modern Approach, 4th US ed.

http://aima.cs.berkeley.edu/index.html

Artificial Intelligence: A Modern Approach, 4th US ed.

Knowledge-Based AI: Cognitive Systems | Udacity Free Courses

At the conclusion of this class, you will be able to accomplish three primary tasks. First, you will be able to design and implement a knowledge-based artificial intelligence agent that can address a complex task using the methods discussed in the course.

Knowledge-Based AI: Cognitive Systems | Udacity Free Courses

https://www.udacity.com/course/knowledge-based-ai-cognitive-systems--ud409

Knowledge-Based AI: Cognitive Systems | Udacity Free Courses

Knowledge Based Artificial Intelligence Part 5 of 5

Share your videos with friends, family, and the world

Knowledge Based Artificial Intelligence Part 5 of 5

https://www.youtube.com/playlist?list=PLAwxTw4SYaPm4zEWxZ-rlepIlLUTCORcn

Knowledge Based Artificial Intelligence Part 5 of 5

Artificial Intelligence Planning

Dr. Gerhard Wickler and Prof. Austin Tate School of Informatics University of Edinburgh Licence: Creative Commons BY-NC, See http://www.aiai.ed.ac.uk/project...

Artificial Intelligence Planning

https://youtube.com/playlist?list=PLwJ2VKmefmxpUJEGB1ff6yUZ5Zd7Gegn2

Artificial Intelligence Planning