This half-semester course aims to introduce *machine learning*, a complex and quickly evolving subject deserving of a far more intensive study. 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.

- Introductions
- What is Learning?
- Experience as Data Visualization
- Boolean Logic vs Fuzzy Logic
- Explicit Programming vs Experiential Training
- Procedural Precision vs Intuitive Approximation
- Inductive and Deductive Reasoning
- Mechanical Induction
- Categories of Machine Learning Algorithms
- Performing Rote Tasks
- Getting Started in Python

- Homework Review
- Linear Algebra Primer
- Getting Started with Plotting in Python and Matplotlib
- Classification as Spatial Partitioning
- A Brief Look at k-means Clustering

- Homework Review
- What is Deep Learning?
- Building Intuition for Machine Learning Problems
- A Brief Tour of Graph Theory
- The Perceptron
- Calculus Primer

- Homework Review
- Multilayer Perceptrons
- Multilayer Perceptron Implementation
- Applying Supervised Learning
- Mapping and Activation Functions
- Unsupervised Learning as a Mediator to Supervised Learning

- Homework Review
- Unsupervised Learning
- Restricted Boltzmann Machine Architecture
- Training Restricted Boltzmann Machines
- Implementing a Restricted Boltzmann Machine

- Homework Review
- Stages of Machine Learning Workflow
- Special Tools and Workflows for Machine Learning
- Streamlining Machine Learning Workflows with Docker
- Getting Started with TensorFlow
- TensorFlow Graphs and Sessions
- TensorFlow Basic Operations
- TensorFlow Working with Data
- TensorFlow Simple Neural Network
- TensorFlow Saving and Restoring

Anderson, Britt. *Computational Neuroscience and Cognitive Modelling: A Student's Introduction to Methods and Procedures.* Los Angeles: SAGE, 2014.

- A Dozen Times Artificial Intelligence Startled The World
- 150+ Machine Learning Tutorials
- Awesome Deep Learning Resources
- Machine Learning From Scratch
- Deep Learning Tutorials
- Effective TensorFlow Tutorials
- Aymeric Damien's TensorFlow Examples
- Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville
- Rules of Machine Learning: Best Practices for ML Engineering
- Distributed Representations of Words and Phrases and their Compositionality by Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean.
- The Unreasonable Effectiveness of Recurrent Neural Networks by Andrej Karpathy
- An Intuitive Explanation of Convolutional Neural Networks
- UFLDL Tutorial: Convolutional Neural Networks

- Coursera: Neural Networks for Machine Learning by Geoffrey Hinton
- Coursera: Machine Learning by Andrew Ng

- Some Basic Mathematics for Machine Learning by Iain Murray and Angela J. Yu
- Math for Machine Learning by Hal Daume
- Machine Learning Math Essentials Part I by Jeff Howbert
- Machine Learning Math Essentials Part II by Jeff Howbert
- Immersive Linear Algebra by J. Ström, K. Åström, and T. Akenine-Möller
- Linear Algebra by Khan Academy
- Probability and Statistics by Khan Academy
- Differential Calculus by Khan Academy

- As We May Think by Vannevar Bush
- A Personal Computer for Children of All Ages by Alan Kay
- The Myth of AI by Jaron Lanier
- Deb Roy: The Birth of a Word
- Physiognomy’s New Clothes
- Why is machine learning hard?

- Python Tutorials
- Python Visualizer
- Python for Programmers
- Introduction to NumPy
- NumPy Tutorial
- Matplotlib Examples

- CreativeAI
- arXiv Machine Learning
- arXiv Neural and Evolutionary Computing
- arXiv Artificial Intelligence
- Reddit Machine Learning
- Reddit Artificial Intelligence
- Deep Learning News
- Google Scholar
- ResearchGate
- NYU Library Online Journal Access

Portions of the above course materials have been excerpted from *Machine Learning for Designers*, a text I published with O’Reilly Media, Inc. in 2016. They have been reprinted on this site with permission from the publisher.