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Computational Neuroscience

Coursera · University of Washington · 2 HN points · 4 HN comments

HN Academy has aggregated all Hacker News stories and comments that mention Coursera's "Computational Neuroscience" from University of Washington.
Course Description

This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab/Octave/Python demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information.

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This course is offered by University of Washington on the Coursera platform.
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Hacker News Stories and Comments

All the comments and stories posted to Hacker News that reference this url.
Here are the resources I found useful: ========================================== Advices from Open AI, Facebook AI leaders

Courses You MUST Take:

Machine Learning by Andrew Ng (https://www.coursera.org/learn/machine-learning) /// Class notes: (http://holehouse.org/mlclass/index.html)

Yaser Abu-Mostafa’s Machine Learning course which focuses much more on theory than the Coursera class but it is still relevant for beginners.

(https://work.caltech.edu/telecourse.html)

Neural Networks and Deep Learning (Recommended by Google Brain Team) (http://neuralnetworksanddeeplearning.com/)

Probabilistic Graphical Models (https://www.coursera.org/learn/probabilistic-graphical-model...)

Computational Neuroscience (https://www.coursera.org/learn/computational-neuroscience)

Statistical Machine Learning (http://www.stat.cmu.edu/~larry/=sml/)

From Open AI CEO Greg Brockman on Quora

Deep Learning Book (http://www.deeplearningbook.org/) ( Also Recommended by Google Brain Team )

It contains essentially all the concepts and intuition needed for deep learning engineering (except reinforcement learning). by Greg

2. If you’d like to take courses: Linear Algebra — Stephen Boyd’s EE263 (Stanford) (http://ee263.stanford.edu/) or Linear Algebra (MIT)

(http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebr...)

Neural Networks for Machine Learning — Geoff Hinton (Coursera) https://www.coursera.org/learn/neural-networks

Neural Nets — Andrej Karpathy’s CS231N (Stanford)

http://cs231n.stanford.edu/

Advanced Robotics (the MDP / optimal control lectures) — Pieter Abbeel’s CS287 (Berkeley)

https://people.eecs.berkeley.edu/~pabbeel/cs287-fa11/

Deep RL — John Schulman’s CS294–112 (Berkeley) http://rll.berkeley.edu/deeprlcourse/

anothertraveler
This list is solid, and could keep you busy for a few years.
Yes, I did my research but there is no such interactive tutorial online like Treehouse or Codecademy. There are so many tutorials but none of it tells you the whole path.

Here are the resources I found useful:

========================================== Advices from Open AI, Facebook AI leaders

Courses You MUST Take: Machine Learning by Andrew Ng (https://www.coursera.org/learn/machine-learning) /// Class notes: (http://holehouse.org/mlclass/index.html)

Yaser Abu-Mostafa’s Machine Learning course which focuses much more on theory than the Coursera class but it is still relevant for beginners.(https://work.caltech.edu/telecourse.html)

Neural Networks and Deep Learning (Recommended by Google Brain Team) (http://neuralnetworksanddeeplearning.com/)

Probabilistic Graphical Models (https://www.coursera.org/learn/probabilistic-graphical-model...)

Computational Neuroscience (https://www.coursera.org/learn/computational-neuroscience)

Statistical Machine Learning (http://www.stat.cmu.edu/~larry/=sml/)

From Open AI CEO Greg Brockman on Quora

Deep Learning Book (http://www.deeplearningbook.org/) ( Also Recommended by Google Brain Team )

It contains essentially all the concepts and intuition needed for deep learning engineering (except reinforcement learning). by Greg

2. If you’d like to take courses: Linear Algebra — Stephen Boyd’s EE263 (Stanford) (http://ee263.stanford.edu/) or Linear Algebra (MIT)(http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebr...)

Neural Networks for Machine Learning — Geoff Hinton (Coursera) https://www.coursera.org/learn/neural-networks

Neural Nets — Andrej Karpathy’s CS231N (Stanford) http://cs231n.stanford.edu/

Advanced Robotics (the MDP / optimal control lectures) — Pieter Abbeel’s CS287 (Berkeley) https://people.eecs.berkeley.edu/~pabbeel/cs287-fa11/

Deep RL — John Schulman’s CS294–112 (Berkeley) http://rll.berkeley.edu/deeprlcourse/

From Director of AI Research at Facebook and Professor at NYU Yann LeCun on Quora

In any case, take Calc I, Calc II, Calc III, Linear Algebra, Probability and Statistics, and as many physics courses as you can. But make sure you learn to program.

JJarrard
Thank you!
atarian
What does physics have to do with ML/AI?
kevinphy
"The Extraordinary Link Between Deep Neural Networks and the Nature of the Universe" https://www.technologyreview.com/s/602344/the-extraordinary-...
Courses You MUST Take:

1. Machine Learning by Andrew Ng (https://www.coursera.org/learn/machine-learning) /// Class notes: (http://holehouse.org/mlclass/index.html)

2. Yaser Abu-Mostafa’s Machine Learning course which focuses much more on theory than the Coursera class but it is still relevant for beginners.(https://work.caltech.edu/telecourse.html)

3. Neural Networks and Deep Learning (Recommended by Google Brain Team) (http://neuralnetworksanddeeplearning.com/)

4. Probabilistic Graphical Models (https://www.coursera.org/learn/probabilistic-graphical-model...)

4. Computational Neuroscience (https://www.coursera.org/learn/computational-neuroscience)

5. Statistical Machine Learning (http://www.stat.cmu.edu/~larry/=sml/)

If you want to learn AI: https://medium.com/open-intelligence/recommended-resources-f...

pedrosorio
If you want to get started with machine learning you MUST take computational neuroscience? I don't think so.
May 08, 2015 · 2 points, 0 comments · submitted by neverminder
Start with a basic course - > https://www.coursera.org/course/compneuro

Then look at PhD thesis in the subject area that are recent. The first section of each PhD thesis covers what is known in the area and builds up the problem. Don't worry yet about understanding the rest of the subject matter - just worry about understanding the front part. Since this is a PhD thesis, it will be very heavily cited. Check out the citations for the front matter and delve into those.

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