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Coursera: Neural Networks for Machine Learning by Geoffrey Hinton.UFLDL Tutorial: Convolutional Neural Networks.An Intuitive Explanation of Convolutional Neural Networks.The Unreasonable Effectiveness of Recurrent Neural Networks by Andrej Karpathy.Distributed Representations of Words and Phrases and their Compositionality by Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean.Rules of Machine Learning: Best Practices for ML Engineering.Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville.A Dozen Times Artificial Intelligence Startled The World.

Additional Resources Machine Learning Tutorials and Resources Computational Neuroscience and Cognitive Modelling: A Student's Introduction to Methods and Procedures.

Streamlining Machine Learning Workflows with DockerĪnderson, Britt.

Special Tools and Workflows for Machine Learning.

Implementing a Restricted Boltzmann Machine.

Restricted Boltzmann Machine Architecture.

Unsupervised Learning as a Mediator to Supervised Learning.

Building Intuition for Machine Learning Problems.

Getting Started with Plotting in Python and Matplotlib.

Categories of Machine Learning Algorithms.

Procedural Precision vs Intuitive Approximation.

Explicit Programming vs Experiential Training.

The goal of this course is to open a preliminary investigation of the conceptual and technical workings of a few key machine learning models, their underlying mathematics, their application to real-world problems and their philosophical value in understanding the general phenomena of learning and experience. This half-semester course aims to introduce machine learning, a complex and quickly evolving subject deserving of a far more intensive study. Learning Machines: Course Syllabus Learning Machines Taught by Patrick Hebron at NYU/ITP, Fall 2017 Previous Editions: Fall 2016, Fall 2015