- (Tom Mitchell "Machine Learning" (recommended by Pierre de Lacaze in his LispNYC presentation, saying that he likes it since it doesn't just talk about what's popular right now, and is a good intro and quite thin))
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"Intro to AI - Introduction to Artificial Intelligence": www.ai-class.com, redirects to Udacity (Instructors: Peter Norvig, Sebastian Thrun)
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(The Stanford AI group also has various other courses, by the way.)
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Stanford University CS224d: Deep Learning for Natural Language Processing (Instructor: Richard Socher) (Bob is working through that one)
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Youtube has the 2015 videos here, but for this year's videos follow the "video" link at the individual sessions on the page linked above.
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Bob so far has watched the first ~8 sessions from 2015, and one (TensorFlow) from 2016.
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Coursera: Machine Learning (Stanford University) (Instructor: Andrew Ng (see People), i.e. Stanford University course CS229a, correct?).
- youtube
- The Stanford lectures are more thorough than those in Coursera.
- materials page: some very good notes of Ng's
Bob might be interested, looks steep.
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CS188: Introduction to Artificial Intelligence (Pieter Abbeel, Dan Klein):
- new website?, index (HN: "mostly focused on good old fashioned AI - things like tree/graph search, constraint satisfaction, logic, some basic graphical models, and a bit of RL/ML at the end". "CS188 vs Udacity's Intro to AI?"--"The Berkeley one is a bit slower and it has a statistics/probability refresher. I found it much easier to follow at 1.5x speed. I'd recommend the Udacity one if your stats/probability knowledge is fresh or if you're re-learning the topics. ")
- Youtube 2015
- index for fall '11
Recommends Russel, Norvig: AI: A Modern Approach as accompanying book (but "this is not a course textbook, things are followed a bit different")
Bob has been through the reinforcement learning bits. Thinks it's good.
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CS231n: Convolutional Neural Networks for Visual Recognition (Stanford, Fei-Fei Li, Andrej Karpathy, Justin Johnson)
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course by Geoffrey Hinton (see People)
- Coursera
- can't find syllabus
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Nando de Freitas has an AI course online: Youtube
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Udacity Deep Learning basically an introduction to TensorFlow (see TensorFlow section in NeuralNetworks).
- CS 179: GPU Programming (CUDA only, it seems) (HN)
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(James) This seems like it could be a nice introduction to neural networks: "Implementing a Neural Network from Scratch - An Introduction" nn-from-scratch
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(James) This drops you half way through chapter 1 if you're not a mathematician. Not recommended!
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Jake Vanderplas' talk at PyCon on scikit-learn. Github has some ipytyhon notebooks you can take whilst watching. Gives a nice intro to the package.