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With Corinna Cortes (Head of Research, Google NY), Dennis Mortensen (Founder, X.ai), Anand Sanwal (Founder, CB Insights), Evan Nisselson (LDV Capital), Josh Sutton (Global Head of AI, Publicis.Sapient), Richard Zemel (Founder, Vectore Institute for AI), Tess Posner (Dir., AI 4 All), Jean-Franois Gagn (CEO, ElementAI).
Monday, October 30, 2017 at 09:00 AM    Cost: $264
Kimmel Ctr @ NYU, 60 Washington Square S
 
     
 
 
              

          
 
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LOCATION
 
DESCRIPTION

NYU students receive a discount. Please email Mina Salib at mina@nyu.edu for the code.

DAY 1 | MONDAY, OCT. 30 Includes all sessions.

Session 1: Intro to Machine Learning with Ross Fadely of Insight Data Science

You will be introduced to some of the core concepts needed to jump into Machine Learning (ML). In addition, we will briefly discuss how teams across many different industries & verticals are using ML in practice. We will dive into some ML case studies using Python & Scikit-Learn. Finally, we will wrap up with some best practices & guidance for continued learning about ML.

You'll leave with,

  • An understanding of how to analyze data using machine learning algorithms.
  • How algorithms should be structured to make the most use of the data you have available

Prerequisites: A working knowledge of Python

Installation requirements: To live run exercises requires a laptop with Python, Numpy, Scipy, & Scikit-Learn installed.

Session 2: Machine Learning for Statisticians withGeorge Lentzas, Ph.D. in Machine Learning from Oxford Currently a Professor of Machine Learning at Columbia University

  • TBD

Session 3: AI in Video Games with Julian Togelius,Associate Professor of Computer Science & Engineering at New York University

  • TBD

Session 4: Intro to Deep Learning. Insturctor: TBD

  • TBD

Session 5: Understanding Linear Regression & Gradient Descent. HOSTED BY

  • The difference between gradient descent & stochasticgradient descent
  • How to use stochastic gradient descent to learn a simple linear regression model
  • How to put together a simple gradient descent using a cost function, allowing you to buildan algorithm for linear regression or a consistent straight line to our data

Session 6: Combining real world & training data. HOSTED BY

  • The pros & cons of training & real world data.
  • How to combine training & real world data to properly train & teach your algorithms

7:00pm - 9:00PM: Future Labs AI Summit Kick Off Party

  • TBD

DAY 2 | TUESDAY, OCT. 31

11:00am - 5:00pm

Included in your ticket purchaseis Day 2 of the Future Labs AI Summit,a full day of presentations & panels with talks from leaders in AI as well as demos from the second cohort of the AI NexusLab. See full details here[INCLUDE LINK].

  • Richard Zemel,Cofounder & Director of Research,Vector Institute for Artificial Intelligence
  • Evan Nisselson, General Partner, LDV Capital
  • Josh Sutton,Global Head, Data & Artificial Intelligence, Publicis.Sapient
  • Anand Sanwal, CEO, CB Insights
  • Jean-Franois Gagn, CEO, ElementAI
 
 
 
 
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