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New Books
- Ad Hoc and Sensor Networks: Theory and Applications (2nd ed.) Carlos de Morais Cordeiro & Dharma Prakash Agrawal
- An Elementary Approach to Design and Analysis of Algorithms Lekh Raj & Shalini Vermani
- An Introduction to Component-Based Software Development Kung-Kiu Lau & Simone di Cola
- An Introduction to the Analysis of Algorithms (3rd ed.) Michael Soltys
- Artificial Intelligence: A Modern Approach (3rd Edition) Stuart Russell et al.
- Computer Architecture: Digital Circuits to Microprocessors Guilherme Arroz, Jose Monteiro & Arlindo Oliveira
- Deep Learning Ian Goodfellow et al.
- Deep Learning with R 1st Edition Francois Chollet
- Deep-Learning Neural Networks: Design and Case Studies Daniel Graupe
- Dynamic Vision: From Images to Face Recognition Shaogang Gong, Stephen J. McKenna, & Alexandra Psarrou
- Exploring Big Historical Data: The Historian's Macroscope Shawn Graham, Ian Milligan, & Scott Weingart
- Fuzzy Logic Theory and Applications Part 1 and Part 2 Lotfi A Zadeh & Rafik A. Aliev
- Hands-On Computer Vision Marc Pomplun
- Image Processing and Analysis - A Primer Georgy Gimel'farb & Patrica Delmas
- Introduction to Evolutionary Informatics Robert J. Marks II, William A. Dembski, & Winston Ewert
- Introduction to Pattern Recognition: Statistical, Structural, Neural and Fuzzy Logic Approaches Menahem Friedman & Abraham Kandel
- Logic and Language Models for Computer Science (3rd ed.) Dana Richards & Henry Hamburger
- Make Your Own Neural Network Tariq Rashid
- Optimization Theory: A Concise Introduction Jiongmin Yong
- Python Machine Learning, 1st Edition Sebastian Raschka
- TensorFlow in 1 Day: Make your own Neural Network Krishna Rungt
- The Nature of Computation Cristopher Moore
- Compilers: Principles, Techniques, and Tools Alfred V. Aho
- Foundations of Machine Learning (2ed) Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar
- Reinforcement Learning (2ed) ichard S. Sutton and Andrew G. Barto
- Machine Learning for Data Streams Albert Bifet, Ricard Gavaldà, Geoff Holmes and Bernhard Pfahringer
- Elements of Causal Inference: Foundations and Learning Algorithms Jonas Peters, Dominik Janzing and Bernhard Schölkopf
- Introduction to Machine Learning (3ed) Ethem Alpaydin
- Machine Learning: A Probabilistic Perspective Kevin P. Murphy
- Foundations of Machine Learning Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar
- Boosting: Foundations and Algorithms Robert E. Schapire and Yoav Freund
- Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation Masashi Sugiyama and Motoaki Kawanabe
- Introduction to Machine Learning, Second Edition Ethem Alpaydin
- Probabilistic Graphical Models: Principles and Techniques Daphne Koller and Nir Friedman
- Introduction to Statistical Relational Learning Lise Getoor and Ben Taskar
- The Minimum Description Length Principle Peter D. Grünwald
- Semi-Supervised Learning Olivier Chapelle, Bernhard Schölkopf and Alexander Zien
- Gaussian Processes for Machine Learning Carl Edward Rasmussen and Christopher K. I. Williams
- Learning Kernel Classifiers: Theory and Algorithms Ralf Herbrich
- Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond Bernhard Schölkopf and Alexander J. Smola
- Principles of Data Mining David J. Hand, Heikki Mannila and Padhraic Smyth
- Bioinformatics, Second Edition: The Machine Learning Approach Pierre Baldi and Søren Brunak
- Causation, Prediction, and Search, Second Edition Peter Spirtes, Clark Glymour and Richard Scheines
- Learning in Graphical Models Michael I. Jordan
- Graphical Models for Machine Learning and Digital Communication Brendan J. Frey
