Our generous publishers are sending books again to MLconf NYC. Make sure to grab coupons, as they’ll be offering exclusive book discounts at MLconf! We’ll be hosting book giveaways at the conclusion of the event for the most unique tweets that mention @mlconf and/or #mlconfnyc. Meet some of the authors, Andreas Mueller will be signing his book Introduction to Machine Learning with Python and our speaker Irina Rish, author of Practical Applications of Sparse Modeling. Participating publishers include: MIT Press, Cambridge University Press. CRC Press, and O’Reilly Media.
Cambridge University Press:
- Agarwal/Chen, Statistical Methods for Recommender Systems
- Barabasi, Network Science
- Bennett & Hugen, Financial Analytics with R
- Braun & Murdoch, Introduction to Statistical Programming with R
- Castillo, Big Crisis Data
- Efron/Hastie, Computer Age Statistical Inference
- Flach, Machine Learning
- Fouss, Algorithms and Models for Network Data and Link Analysis
- Guenin e al, A Gentle Introduction to Optimization
- Leskovec et al, Mining of Massive Data Sets
- Liu, Sentiment Analysis
- Roughgarden, Twenty Lectures on Algorithmic Game Theory
- Wainer, Truth or Truthiness
- Warwick & Shah, Turing’s Imitation Game
- Watt, Machine Learning Refined
CRC Press:
*Save 20% when ordering online and enter promo code: AWR96
- A First Course in Machine Learning, Second Edition
- Autonomous Vehicle Navigation: From Behavioral to Hybrid Multi-Controller Architectures
- Big Data and Social Science: A Practical Guide to Methods and Tools
- Data Mining: A Tutorial-Based Primer, Second Edition
- Data Mining with R: Learning with Case Studies, Second Edition
- Machine Learning: An Algorithmic Perspective, Second Edition
- Machine Learning: Algorithms and Applications
- Modern Data Science with R
- Statistical Learning with Sparsity: The Lasso and Generalizations
- Sparse Modeling: Theory, Algorithms, and Applications
MIT:
- Introduction to Machine Learning, Third Edition
- Deep Learning
- Perturbations, Optimization, and Statistics
- Fundamentals of Machine Learning for Predictive Data Analytics
- Decision Making Under Uncertainty
- Foundations of Machine Learning
- Machine Learning
- Advanced Structured Prediction
- Practical Applications of Sparse Modeling, MLconf NYC Speaker, Irina Rish
- Boosting
- Optimization for Machine Learning
- Machine Learning in Non-Stationary Environments
OReilly Media:
* Use discount code: PCBW and save 40% on books, 50% on ebooks and videos.
Additional Machine Learning Books on Display:
- How to Create a Mind: The Secret of Human Thought Revealed, Kurzweil, Ray
- Overcomplicated: Technology at the Limits of Comprehension, Arbesman, Samuel
- Artificial Intelligence Simplified: Understanding Basic Concepts, George, Dr Binto
- Rise of the Robots: Technology and the Threat of a Jobless Future, Ford, Martin
- Superintelligence: Paths, Dangers, Strategies, Bostrom, Nick
- Our Final Invention: Artificial Intelligence and the End of the Human Era, Barrat, James
- The Age of Spiritual Machines: When Computers Exceed Human Intelligence, Kurzweil, Ray
- The Singularity Is Near: When Humans Transcend Biology, Kurzweil, Ray
- The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future, Kelly, Kevin
- Our Robots, Ourselves: Robotics and the Myths of Autonomy, Mindell, David A.
