Neural Networks By: Colleen McGuckin

https://docs.google.com/a/erhsnyc.net/presentation/d/15nmoPcXYwLXLfWYWIG2-NH_N8_dcpn1fliwa25rZq3Q/edit?usp=sharing

This is my presentation on Neural Networks, feel free to ask me questions about it, or if you find something else about neural networks, tell me and I can add to the slides!

Machine Learning: Linear Regression

Decision Trees

Here is my powerpoint showing the basics of decision trees

Decision Trees

Clustering Presentation by Julia Zee and Alyssa Koo

Machine Learning Seminar I (03/13/2017)

 You are encouraged to invite friends to participate the seminar. 

                                               Speakers: 

• Nearest Neighbor                   Egzon Mujaj 
• Linear regression                   Emma Ruzicka 
•  Decision Trees                      Bailey McElligott 
• Support Vector Machines       Chin-Sung Lin 
• Neural Network                     Colleen McGuckin 
• Clustering                              Alyssa Koo & Julia Zee

Notes: A DARPA Perspective on Artificial Intelligence - Emma

In the video, John Launchbury presents the abilities, limits, and future of technology through comparing its different forms, as it has advanced through the years. Nevertheless, it is important to note that all waves, as he described them, are still crucial in research, development, technology, and new inventions.

1. First Wave (Handcrafted knowledge)
1. Characterizing human knowledge into rules that could be implemented and interpreted by a computer
2. Pro: Reasoning over narrowly defined domains (Reasoning)
3. Cons: Poor handling of uncertainty, no learning capability, cannot interpret and predict the natural world/work with probability (Perceiving, Learning, Abstracting)
4. Ex: game-playing programs, Turbotax, cybersecurity, etc. 
2. Second Waves (Statistical Learning)
1. Creating and training statistical models (ex.: NEURAL NETWORKS: manifold hypothesis, "spreadsheets on steroids"), which learn through data inputs
2. Pros: Perceiving the natural word and learning from it, learning and adapting based on data on constant and new data, classify & predict (Perceiving, Learning)
3. Cons: Cannot apply knowledge into multiple directions, not as reliable individually as they are reliable statistically --> prone to mistakes due to working based on probability,  (Abstracting, Reasoning)
4. Ex: Face and Voice recognition, network flows
3. Third Wave (Future, Contextual Adaptation)
1. A responsive system to help understand decision-making trained through data inputs or examples
2. 

Key to Pros & Cons:

• Perceive: Observing and understanding the outside world 
• Learning: Constant gain and incorporation of knowledge
• Abstracting: Using acquired knowledge at different levels of a problem
• Reasoning: working through facts, logical reasoning

A DARPA Perspective on AI

Defense Advanced Research Projects Agency (DARPA) is one of the main driving force of new technology in the US. They had hosted the Grand Challenge in 2004 and 2005 to accelerate the breakthrough of driver-less car technology. Here is a concise and in-depth analysis of AI technology from DARPA. Using perceiving, learning, abstracting and reasoning as measurement, John Launchbury, the Director of DARPA's Information Innovation Office (I2O), attempts to demystify AI- what it can do, what it can't do, and where it is headed. Let's learn from his analysis of the "three waves of AI". You are encouraged to take notes and share it with us on blog.

Machine Learning Seminar I: Introduction to Machine Learning

It's great to see some of you posting your results of the Perceptron Challenge onto our blog! It's an amazing achievement for picking up a new tech skill so fast! I encourage the rest of you continue posting your results if you haven't done so.

Before we diving into a very specific Machine Learning (ML) technique called Multi-Layer Perceptron (MLP), it is reasonable to first broaden our general knowledge and perspective of Machine Learning. We are going to hold a Machine Learning Seminar to fulfill this purpose. Group members are welcome to choose specific topics in ML and presents them in our seminar. You will go deeper in certain topic(s), and we are going to learn from each other. Please use the following link to sign up for the topic(s) you are going to present. You can pair with another group member.

The details of the seminar is listed below:

• Date: March 13, 2017 (Monday)
• Time: 4:00 pm - 6:00 pm
• Length: 10 minutes for each topic including Q&A
• Content: Explaining a specific ML technique
• Format: PowerPoint or Google Slides
• Audience: AI Research Group + invited friends

You may use other online resource to help you prepare for the presentation. You may explain both the theoretical part (algorithm) and application part of that ML technique (run through an example). If you have any questions, please feel free to contact me.

Intuitive AI and Augmented Age

How AI can impact our lives? Listen to futurist Maurice Conti painting the future of AI - Augmented Age for us. Several innovative ideas has been presented in this TED Talk: generative AI, intuitive AI, human-robot augmentation, product nerve system, etc. Sit back and enjoy this inspiring intellectual journey!