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Artificial Intelligence (AI)

edX · Columbia University · 3 HN comments

HN Academy has aggregated all Hacker News stories and comments that mention edX's "Artificial Intelligence (AI)" from Columbia University.
Course Description

Learn the fundamentals of Artificial Intelligence (AI), and apply them. Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems.

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This course is offered by Columbia University on the edX platform.
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Buy and work through "Artificial Intelligence: A Modern Approach". It's a huge book and the de facto standard for pretty much every AI 101+ course. Some of the stuff may not interest you some might but it covers a broad range (from logic based agents to Bayesian networks). It's systematic and has excellent references and further reading notes for each chapter. The focus is not on the currently sexy "data science" aspects though (however you will find plenty of material that is relevant).

The edX class from Berkeley is pretty fun and hands on. It uses Pacman as a running example and essentially teaches the agents stuff from AIAMA:

The Stanford class by Thrun and Norvig himself (one of the authors of AIAMA) is also good but I prefer the edX one:

Edit: changed to direct links for the courses

+1 for the Stanford course. Great intro to AI and super easy to follow - I've done it after my uni class elsewhere, and it helped to internalise what I've learned there.
reading the book still requires a significant amount of time
The AIMA book is sort of a Good Old-Fashioned AI (GOFAI) book that focuses a lot on agents and planning. The jobs this article is talking about are really machine learning ones-- taking large volumes of data and extracting knowledge, so as to build recommender systems and such. For that, Kevin Murphy's book, "Machine Learning: A Probabilistic Approach" is without a doubt the best book out there, both in terms of explaining things from the ground up and being the most comprehensive/up-to-date source.
Murphy's book is actually subtitled "A Probabilistic Perspective" -- "Machine Learning: A Probabilistic Approach" is a different book by a different author.
There's still quite a bit of material on Bayesian networks (with the dreadfull dentist example :D), neural networks and support vector machines but overall you're right the focus is on agents. The relevant chapters are great staring points though and as always filled with great reference material for further reading.

+ I'm pretty sure if you apply for an AI job somewhere and it's labaled AI and not "data science" they'll expect that you know the material in AIAMA.

It's amazing. Although quite time-consuming, the projects are nice: it was building an AI for Pac-man to maximize its score in various scenarios.

The next offering is starting soon:

The pricey but standard recommendation would be buying and working through "Artificial Intelligence - A Modern Approach". It's one of my favourite IT books :)

There's a pretty fun/good course on one of the free MOOCs that pretty much follows the book and uses pacman as an example. It uses Python. It's the course that has a cute robot on the slides :) Edit (found it, this one):

Edit2: There's also this one but it's not the one I'm thinking of (it's also good though, Norvig is one of the authors of AI-AMA):

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