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Class Guide for Graduate Seminar of Dr. Alina Lazar: New Books

General Information for graduate student at Computer Science for their study and research.

New Books

  • Ad Hoc and Sensor Networks: Theory and Applications (2nd ed.)              Carlos de Morais Cordeiro & Dharma Prakash Agrawal
    Call no.: TK7872.D48 C68 2011x  
  • An Elementary Approach to Design and Analysis of Algorithms  Lekh Raj & Shalini Vermani 
    Call no.:  
  • An Introduction to Component-Based Software Development   Kung-Kiu Lau & Simone di Cola
    Call no.: QA76.76.C66 L38 2018
  • An Introduction to the Analysis of Algorithms (3rd ed.) Michael Soltys
    Call no.: QA9.58 .S63 2018  
  • Artificial Intelligence: A Modern Approach (3rd Edition) Stuart Russell et al.
    Call no.: Q335 .R86 2010   
  • Computer Architecture: Digital Circuits to Microprocessors         Guilherme Arroz, Jose Monteiro & Arlindo Oliveira
    Call no.: QA76.9.C62 A77 2019  
  • Deep Learning   Ian Goodfellow et al.
    Call no.:  
  • Deep Learning with R 1st Edition              Francois Chollet
    Call no.: QA276.45.R3 C46 2018  
  • Deep-Learning Neural Networks:  Design and Case Studies          Daniel Graupe
    Call no.: QA76.87 .G73 2016  
  • Dynamic Vision:  From Images to Face Recognition          Shaogang Gong, Stephen J. McKenna, & Alexandra Psarrou
    Call no.: TA1650 .G66 2000  
  • Exploring Big Historical Data:  The Historian's Macroscope           Shawn Graham, Ian Milligan, & Scott Weingart
    Call no.: D16.117 .G73 2016  
  • Fuzzy Logic Theory and Applications Part 1 and Part 2    Lotfi A Zadeh & Rafik A. Aliev
    Call no.: QA9.64.117 .Z33 2019
  • Hands-On Computer Vision        Marc Pomplun
    Call no.:  QA76.73.J8 2019
  • Image Processing and Analysis - A Primer             Georgy Gimel'farb & Patrica Delmas
    Call no.: TA1630 .G555 2019 
  • Introduction to Evolutionary Informatics              Robert J. Marks II, William A. Dembski, & Winston Ewert
    Call no.: TA347.E96 M37 2018 
  • Introduction to Pattern Recognition: Statistical, Structural, Neural and Fuzzy Logic Approaches      Menahem Friedman & Abraham Kandel
    Call no.: TK7882.P3 F75 2019  
  • Logic and Language Models for Computer Science (3rd ed.)         Dana Richards & Henry Hamburger
    Call no.: QA267.3 .R53 2017x 
  • Make Your Own Neural Network             Tariq Rashid
    Call no.:  QA76.87 .R37 2016x  
  • Optimization Theory:  A Concise Introduction    Jiongmin Yong
    Call no.: QA402.5 .Y66 2018  
  • Python Machine Learning, 1st Edition     Sebastian Raschka
    Call no.: QA76.73.P98 R37 2015x  
  • TensorFlow in 1 Day: Make your own Neural Network   Krishna Rungt
    Call no.: QA76.87 .R86 2018x  
  • The Nature of Computation        Cristopher Moore
    Call no.: QA267.7 .M66 2016x  
  • Compilers: Principles, Techniques, and Tools       Alfred V. Aho
    Call no.:  QA76.76.C65 A37 2017 
  • Foundations of Machine Learning (2ed) Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar
    Call no.:  Q325.5 .M64 2012    
  • Reinforcement Learning (2ed)   ichard S. Sutton and Andrew G. Barto
    Call no.: Q325.6 .R45 2018  
  • Machine Learning for Data Streams        Albert Bifet, Ricard Gavaldà, Geoff Holmes and Bernhard Pfahringer 
    Call no.: QA76.9.D343 B54 2017  
  • Elements of Causal Inference: Foundations and Learning Algorithms      Jonas Peters, Dominik Janzing and Bernhard Schölkopf 
    Call no.: Q325.5 .P48 2017  
  • Introduction to Machine Learning (3ed) Ethem Alpaydin 
    Call no.: Q325.5 .A46 2014  
  • Machine Learning: A Probabilistic Perspective   Kevin P. Murphy 
    Call no.:  Q325.5 .M87 2012  
  • Foundations of Machine Learning            Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar
    Call no.: Q325.5 .M64 2012  
  • Boosting: Foundations and Algorithms  Robert E. Schapire and Yoav Freund 
    Call no.: Q325.75 .S33 2014  
  • Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation Masashi Sugiyama and Motoaki Kawanabe 
    Call no.: Q325.5 .S845 2012
  • Introduction to Machine Learning, Second Edition            Ethem Alpaydin 
    Call no.: Q325.5 .A46 2014  
  • Probabilistic Graphical Models: Principles and Techniques          Daphne Koller and Nir Friedman 
    Call no.: QA279.5 .K65 2019  
  • Introduction to Statistical Relational Learning    Lise Getoor and Ben Taskar 
    Call no.: QA76.9.D3 I68 2017  
  • The Minimum Description Length Principle         Peter D. Grünwald 
    Call no.: Q325.75 .S33 2014  
  • Semi-Supervised Learning           Olivier Chapelle, Bernhard Schölkopf and Alexander Zien 
    Call no.: Q325.75 .S42 2010 
  • Gaussian Processes for Machine Learning            Carl Edward Rasmussen and Christopher K. I. Williams 
    Call no.: QA274.4 .R37 2006  
  • Learning Kernel Classifiers: Theory and Algorithms          Ralf Herbrich 
    Call no.: Q325.5 .H48 2002  
  • Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond       Bernhard Schölkopf and Alexander J. Smola 
    Call no.:  Q325.5 .S32 2002   
  • Principles of Data Mining             David J. Hand, Heikki Mannila and Padhraic Smyth 
    Call no.: QA76.9.D343 H38 2001  
  • Bioinformatics, Second Edition: The Machine Learning Approach              Pierre Baldi and Søren Brunak 
    Call no.:  QH506 .B35 2001  
  • Causation, Prediction, and Search, Second Edition            Peter Spirtes, Clark Glymour and Richard Scheines 
    Call no.:  QA276 .S65 2000 
  • Learning in Graphical Models    Michael I. Jordan 
    Call no.: QA279 .L375 1999
  • Graphical Models for Machine Learning and Digital Communication       Brendan J. Frey 
    Call no.:  Q325.5 .F74