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. Participating publishers include: Now Publishers, MIT Press, Cambridge University Press. CRC Press, Springer and O’Reilly Media.
Now Publishers
Learning Deep Architectures for AI, Yoshua Bengio, now publishers
Bayesian Reinforcement Learning: A Survey, Mohammad Ghavamzadeh | Shie Mannor | Joelle Pineau | Aviv Tamar, now publishers
An Introduction to Conditional Random Fields, Charles Sutton | Andrew McCallum, now publishers
Kernels for Vector-Valued Functions: A Review, Mauricio A. Álvarez | Lorenzo Rosasco | Neil D. Lawrence, now publishers
Online Learning and Online Convex Optimization, Shai Shalev-Shwartz, now publishers
Convex Optimization: Algorithms and Complexity, Sébastien Bubeck, now publishers
An Introduction to Matrix Concentration Inequalities, Joel A. Tropp, now publishers
Explicit-Duration Markov Switching Models, Silvia Chiappa, now publishers
Adaptation, Learning, and Optimization over Networks, Ali H. Sayed, now publishers
Theory of Disagreement-Based Active Learning, Steve Hanneke, now publishers
From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning, Rémi Munos, now publishers
Learning with Submodular Functions: A Convex Optimization Perspective, Francis Bach, now publishers
Backward Simulation Methods for Monte Carlo Statistical Inference, Fredrik Lindsten | Thomas B. Schön, now publishers
Graphical Models, Exponential Families, and Variational Inference, Martin J. Wainwright | Michael I. Jordan, now publishers
MIT Press
Introduction to Machine Learning, third edition, hc, Alpaydin, MIT Press
Building Ontologies with Basic Formal Ontology, pb, Arp, MIT Press
Signals and Boundaries, pb, Holland, MIT Press
Fundamentals of Machine Learning for Predictive Data Analytics, hc, Kelleher, MIT Press
Foundations of Machine Learning, hc, Mohri, MIT Press
Advanced Structured Prediction, hc, Nowozin, MIT Press
Practical Applications of Sparse Modeling, hc, Rish, MIT Press
Boosting, pb, Schapire, MIT Press
Optimization for Machine Learning, hc, Sra, MIT Press
Machine Learning in Non-Stationary Environments, hc, Sugiyama, MIT Press
Artificial Cognitive Systems, hc, Vernon, MIT Press
Cambridge University Press
Statistical Methods for Recommender Systems, Agarwal | Chen, Cambridge University Press
Interactions with Search Systems, White, Cambridge University Press
Introduction to Random Graphs, Frieze | Karonski, Cambridge University Press
Computational Social Science, Alvarez, Cambridge University Press
Privacy, Big Data, and the Public Good, Lane et al, Cambridge University Press
Machine Learning, Flach, Cambridge University Press
Understanding Machine Learning, Shalev-Shwartz | Ben-David, Cambridge University Press
Data Mining and Analysis, Zaki | Meira, Cambridge University Press
Bayesian Reasoning and Machine Learning, Barber, Cambridge University Press
Mining of Massive Data Sets, Leskovec et al, Cambridge University Press
Social Media Mining, Zafarani et al, Cambridge University Press
Truth or Truthiness, Wainer, Cambridge University Press
Twitter: A Digital Socioscope, Mejova et al, Cambridge University Press
Causal Inference, Imbens | Rubin, Cambridge University Press
A Gentle Introduction to Optimization, Guenin et al, Cambridge University Press
CRC Press
Text Mining and Visualization: Case Studies Using Open Source Tools, Hoffman | Chisholm, CRC Press
Handbook of Big Data, Bühlmann | Drineas | Kane | Laan, CRC Press
Accelerating Discovery: Mining Unstructured Information for Hypothesis Generation, Spangler, CRC Press
Statistical Learning with Sparsity: The Lasso and Generalizations, Hastie | Tibshirani | Wainwright, CRC Press
Statistical Reinforcement Learning: Modern Machine Learning Approaches, Sugiyama, CRC Press
Machine Learning: An Algorithmic Perspective, Second Edition, Marsland, CRC Press
Sparse Modeling: Theory, Algorithms, and Applications, Rish | Grabarnik, CRC Press
Computational Trust Models and Machine Learning, Liu | Datta | Lim, CRC Press
Regularization, Optimization, Kernels, and Support Vector Machines, Suykens | Signoretto | Argyriou, CRC Press
Data Classification: Algorithms and Applications, Aggarwal, CRC Press
Springer
Data Mining: The Textbook, Aggarwal, Springer
Bayesian Computation with R, Albert, Springer
Learning with Partially Labeled and Interdependent Data, Amini | Usunier, Springer
Text Mining with MATLAB®, Banchs, Springer
Pattern Recognition and Machine Learning, Bishop, Springer
Principles of Data Mining, Bramer, Springer
Machine Learning in Medicine – Cookbook, Cleophas | Zwinderman, Springer
Robotics, Vision and Control: Fundamental Algorithms in MATLAB, Corke, Springer
Introduction to Evolutionary Computing, Eiben | Smith, Springer
Chance Rules: An Informal Guide to Probability, Risk and Statistics, Everitt, Springer
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Hastie | Tibshirani | Friedman, Springer
An Introduction to Statistical Learning, James | Witten | Hastie | Tibshirani, Springer
Statistical Analysis of Network Data with R, Kolaczyk | Csárdi, Springer
An Introduction to Machine Learning, Kubat, Springer
Twitter Data Analytics, Kumar | Morstatter | Liu, Springer
Web Data Mining, Liu, Springer
Big Data: A Primer, Mohanty | Bhuyan | Chenthati, Springer
Big Data Imperatives, Mohanty | Jagadeesh | Srivatsa, Springer
Bayesian Networks in R, Nagarajan | Scutari | Lèbre, Springer
Emerging Paradigms in Machine Learning, Ramanna | Jain | Howlett, Springer
Diffusion in Social Networks, Shakarian | Bhatnagar | Aleali | Shaabani | Guo, Springer
All of Statistics, Wasserman, Springer
Data Mining with Rattle and R, Williams, Springer
A Beginner’s Guide to R, Zuur | Ieno | Meesters, Springer
Misc Publishers
How to Create a Mind: The Secret of Human Thought Revealed, Kurzweil
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World, Domingos
Big Data: Principles and Best Practices of Scalable Realtime Data Systems, Marz | Warren
Superforecasting: The Art and Science of Prediction, Tetlock, Gardner
Data Smart: Using Data Science to Transform Information into Insight, Foreman
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, Provost | Fawcett
Data Science from Scratch: First Principles with Python, Grus
Competing on Analytics: The New Science of Winning, Harris | Davenport
Naked Statistics: Stripping the Dread from the Data, Wheelan
The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t, Silver
Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, Siegal
The End of Average: How We Succeed in a World That Values Sameness, Rose
