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https://github.com/nature-of-code/NOC-S17-2-Intelligence-Learning

Syllabus for Part 2 of Nature of Code: "Intelligence and Learning" at ITP Spring 2017 Edit
https://github.com/nature-of-code/NOC-S17-2-Intelligence-Learning

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Syllabus for Part 2 of Nature of Code: "Intelligence and Learning" at ITP Spring 2017 Edit

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README 

        
The Nature of Code Part 2 (Spring 2017)

======================================



Syllabus for Part 2 of Nature of Code: "Intelligence and Learning" at ITP Spring 2017



This syllabus is very much in progress. I'm drawing inspiration from this [Coding Train community list of resources](https://github.com/CodingTrain/Machine-Learning).



Class Info

----------

* Daniel Shiffman: [email protected]

* Section 1: Tuesdays, 9:00-11:30am

* Section 2: Wednesdays, 9:00-11:30am

* [Office Hours](https://itp.nyu.edu/inwiki/Signup/Shiffman)

* [Sign up for Mailing List](https://groups.google.com/a/itp.nyu.edu/forum/#!forum/natureofcode)

* [Resources and References](https://github.com/shiffman/NOC-S17-2-Intelligence-Learning/wiki/References-Resources)



Prerequisites?

-------------

* Basic familiary with [p5.js](http://p5js.org) and [Processing](http://processing.org).

* Fundamentals of computer programming, equivalent to [ICM](https://github.com/ITPNYU/ICM-2016)

* While taking [Nature of Code Part 1](https://github.com/shiffman/NOC-S17-1-Physics-Animation) is not required, I recommend you familiarize yourself with the following chapters before the first day of class.

  * [Chapter 1: Vectors](http://natureofcode.com/book/chapter-1-vectors)

  * [Chapter 2: Forces](http://natureofcode.com/book/chapter-2-forces)

  * [Chapter 3: Oscillation and Trig](http://natureofcode.com/book/chapter-3-oscillation)

  * [Chapter 6: Steering Behaviors](http://natureofcode.com/book/chapter-6-autonomous-agents)

* I assume no knowledge whatsoever about AI, machine learning, deep learning and the accompanying math required for the algorithms listed below. After all, I barely know this stuff myself.



Week 1 - Introduction (March 21/22)

-------------------------------

* [Week 1 Notes](https://github.com/shiffman/NOC-S17-2-Intelligence-Learning/blob/master/week1-graphs/README.md)

* [Week 1 Homework](https://github.com/shiffman/NOC-S17-2-Intelligence-Learning/wiki/Homework-1)

* Class Intro / Overview

* Algorithms

  * Big O notation

  * Graphs

     * [Binary Tree](https://en.wikipedia.org/wiki/Binary_tree)

     * [Breadth-first Search](https://en.wikipedia.org/wiki/Breadth-first_search)

     * [Dijktra's Algorithm](https://en.wikipedia.org/wiki/Dijkstra's_algorithm)

     * [A\* search](https://en.wikipedia.org/wiki/A*_search_algorithm)

     * [Traveling Salesperson](https://en.wikipedia.org/wiki/Travelling_salesman_problem)

     * plus steering agents!



Week 2 - Genetic Algorithms (March 28/29)

---------------------------

* [Week 2 Notes](https://github.com/shiffman/NOC-S17-2-Intelligence-Learning/blob/master/week2-evolution/README.md)

* [Week 2 Homework](https://github.com/shiffman/NOC-S17-2-Intelligence-Learning/wiki/Homework-2)

* Search

* Evolutionary Design

* Evolutionary Ecosystem



Week 3 - Classification and Regression (April 4/5)

-------------------------------

* [Week 3 Notes](https://github.com/shiffman/NOC-S17-2-Intelligence-Learning/blob/master/week3-classification-regression/README.md)

* [Week 3 Homework](https://github.com/shiffman/NOC-S17-2-Intelligence-Learning/wiki/Homework-3)

* What is Machine Learning

* What is Supervised Learning

* Classification and Regression

* KNN

* Linear Regression and Gradient Descent



Week 4 - Neural Networks (April 11/12)

------------------------

* [Week 4 Notes](https://github.com/shiffman/NOC-S17-2-Intelligence-Learning/blob/master/week4-neural-networks/README.md)

* Perceptron

* Multi-Layered Perceptron

* inputs and outputs

* Backpropogation

* Training vs. Testing (MNIST data set)

* What is "Deep Learning"?



Week 5 - Adding Tensorflow: Convolutional Neural Networks (April 18/19)

-----------------------------

* [Environment Setup](https://github.com/shiffman/NOC-S17-2-Intelligence-Learning/wiki/Python-Environment-Setup)

* [Week 5 Notes](https://github.com/shiffman/NOC-S17-2-Intelligence-Learning/blob/master/week5-cnn-tensorflow/README.md)

* [Assignment: Project Step 1](https://github.com/shiffman/NOC-S17-2-Intelligence-Learning/wiki/Project-Step-1)

* Overview of libraries and frameworks for Deep Learning

* Convolutional Neural Networks for Image Classification (and more)

* Keras and Tensorflow

* Python and Flask

* Flask and p5.js



Week 6 - Recurrent Neural Networks, NeuroEvolution/Reinforcement Learning (April 25/26)

--------------------------

* [Week 6 RNN Notes](https://github.com/shiffman/NOC-S17-2-Intelligence-Learning/blob/master/week6-rnn-tensorflow/README.md)

* [Week 6 Bonus NeuroEvolution Notes](https://github.com/shiffman/NOC-S17-2-Intelligence-Learning/tree/master/week6bonus-reinforcement-neuroevolution/README.md)

* Recurrent Neural Networks for Sequences (text generation)

* Overview of Reinforcement Learning

* Neuro Evolution (evolving ANN weights)



Week 7 - Project Presentations (May 2/3)

------------------------------

* [Project Presentations + Documentation](https://github.com/shiffman/NOC-S17-2-Intelligence-Learning/wiki/Project)



Policies

-----------------------------------------------

* Submit assignments by the evening before class to the extent possible.

* Come prepared with questions.

* Put away screens during others' presentations.

* Participate!

* Document!

* Grading:

   * 40% Class Participation

   * 40% Quality of assignments

   * 20% Final project

* For a 2-point class, 2 or more unexcused absences is grounds for failure.