Call For Speakers- 2017 Events

• Algorithms that have graduated academic conferences such as NIPS, ICML, etc and have proven to be effective, robust and scalable in production
• Novel data science practices, such as data transformations, new data sources, novel representations, etc
• Machine Learning/AI case studies (Lessons learned), demonstrating challenges in the wild and how to handle them in a new way
• New platforms, tools for machine learning. Emphasis should be given on the technical challenges, benchmarks and motivation for the development
• New business practices, for managing, growing data science teams and expanding machine learning to new domains
• Tutorials and novel ways of presenting and simplifying machine learning domains, including: deep learning, kernel methods, bayesian nonparametrics, tensor algebra, etc
• Up and coming areas of machine learning that you think will dominate the industry in the future, such as probabilistic programming etc.

Topics we’re looking for:

• Natural Language Processing
• Deep Learning
• Reinforcement Learning
• Neural Turing Machines/Neural Theorem Provers
• Generative Models
• Probabilistic Programming
• Probabilistic Logic and Reasoning
• Chatbots/ ALL Bots
• Bayesian Inference
• One Shot Learning
• Markov Logic Networks
• Structure Learning
• Synthetic Art, Biology
• Ethics in Machine Learning
• Sketching Randomized Algorithms
• Game Theory
• Community Detection
• Large-Scale Clustering
• Time Series
• Image Analysis
• Bayesian Non-Parametrics
• Topic Models

1. Clarity and novelty of the presentation
2. Diversity of the topics for the conference
3. Speaker’s experience in the industry