Books available @ Institute for Machine Learning
Signature | Author(s) | Titel | Year |
2730 | Hansen, Andrea | Bioinformatik - Ein Leitfaden für Naturwissenschaftler | 2001 |
18982 | Cristianini, Nello | An introduction to support vector machines | 2006 |
18987.1 | Lengauer, Thomas (ed.) | Bioinformatics - From Genome to Drugs (Vol. I) | 2002 |
18987.2 | Lengauer, Thomas (ed.) | Bioinformatics - From Genome to Drugs (Vol. II) | 2002 |
18988 | Augen, Jeff | Bioinformatics in the post-genomic era | 2005 |
18989 | Pevsner, Jonathan | Bioinformatics and functional genomics | 2003 |
18990 | Gusfield, Dan | Algorithms on strings, trees, and sequences: computer science and computational biology | 1999 |
18991 | Voet, Donald | Biochemistry | 2004 |
18992 | Paun, Gheorghe | DNA computing: new computing paradigms | 2005 |
18993 | Clote, Peter | Computational molecular biology: an introduction | 2005 |
18994 | Baldi, Pierre | DNA microarrays and gene expression: from experiment to data analysis and modeling | 2002 |
18995 | Friesner, Richard A. (ed.) | Advances in chemical physics - Computational Methods for Protein Folding | 2001 |
18996 | Weston, Paul | Bioinformatics software engineering: delivering effective applications | 2004 |
18997 | Baldi, Pierre | Bioinformatics: the machine learning approach | 2001 |
18998 | Korf, Ian | BLAST | 2003 |
18999 | Lacroix, Zoé | Bioinformatics: managing scientific data | 2003 |
19000 | Baxevanis, Andreas D. | Bioinformatics: a practical guide to the analysis of genes and proteins | 2005 |
19001 | Petsko, Gregory A. | Protein structure and function | 2004 |
19002 | Kohane, Isaac S. | Microarrays for an integrative genomics | 2003 |
19003 | Setubal, João Carlos | Introduction to computational molecular biology | 1997 |
19004 | Schölkopf, Bernhard | Advances in kernel methods: support vector learning | 1999 |
19005 | Schölkopf, Bernhard | Learning with Kernels: support vector machines, regularization, optimization, and beyond | 2002 |
19006 | Nei, Masatoshi | Molecular evolution and phylogenetics | 2000 |
19007 | Wang, Jason T. L. | Pattern discovery in biomolecular data: tools, techniques, and applications | 1999 |
19010 | Wit, Ernst | Statistics for microarrays: design, analysis and inference | 2004 |
19011 | Hyvärinen, Aapo | Independent component analysis | 2001 |
19012 | Sorensen, Daniel | Likelihood, Bayesian, and MCMC methods in quantitative genetics | 2002 |
19013 | Eidhammer, Ingvar | Protein bioinformatics: an algorithmic approach to sequence and structure analysis | 2004 |
19014 | Duda, Richard O. | Pattern classification | 2001 |
19015 | Dwyer, Rex A. | Genomic Perl: from bioinformatics basics to working code | 2003 |
19016 | Lesk, Arthur M. | Introduction to protein architecture: the structural biology of proteins | 2003 |
19017 | MacKay, David J. C. | Information theory, inference, and learning algorithms | 2005 |
19018 | Jones, Neil C. | An introduction to bioinformatics algorithms | 2004 |
19019 | Cherkassky, Vladimir | Learning from data: concepts, theory, and methods | 1998 |
19020 | Bishop, Christopher M. | Neural networks for pattern recognition | 2005 |
19021 | Hand, David J. | Principles of data mining | 2001 |
19022 | Sutton, Richard S. | Reinforcement learning: an introduction | 1998 |
19024 | Kolchanov, Nikolay (ed.) | Bioinformatics of genome regulation and structure | 2004 |
19025 | Branden, Carl | Introduction to protein structure | 1999 |
19026 | Mathews, Christopher K. | Biochemistry | 2000 |
19030 | Cichocki, Andrzej | Adaptive blind signal and image processing: learning algorithms and applications | 2005 |
19031 | Vapnik, Vladimir N. | Statistical learning theory | 1998 |
19032 | Knudsen, Steen | Guide to analysis of DNA microarray data | 2004 |
19033 | Deco, Gustavo | An information-theoretic approach to neural computing | 1997 |
19034 | Golub, Gene H. | Matrix computations | 1996 |
19035 | Haykin, Simon (ed.) | Unsupervised adaptive filtering | 2000 |
19036 | Lütkepohl, Helmut | Handbook of matrices | 1996 |
19037 | Suykens, Johan A. | Least squares support vector machines | 2005 |
19038 | Haykin, Simon | Neural networks: a comprehensive foundation | 1999 |
19041 | Vapnik, Vladimir N. | The nature of statistical learning theory | 2000 |
19042 | Joachims, Thorsten | Learning to classify text using support vector machines: methods, theory and algorithms | 2002 |
19043 | Lee, Te-Won | Independent component analysis: theory and applications | 1998 |
19044 | Jolliffe, Ian T. | Principal component analysis | 2002 |
19045 | Hertz, John | Introduction to the theory of neural computation | 1991 |
19052 | Hastie, Trevor | The elements of statistical learning: data mining, inference, and prediction | 2001 |
19059 | Haykin, Simon S. | Unsupervised adaptive filtering, Vol. II | 2000 |
19071 | Herbrich, Ralf | Learning kernel classifiers: theory and algorithms | 2002 |
19075 | Barnes, Michael R. | Bioinformatics for geneticists | 2003 |
19076 | Neal, Radford M. | Bayesian learning for neural networks | 1996 |
19077 | Wang, Jason T. L. | Data mining in bioinformatics | 2005 |
19078 | Ewens, Warren J. | Statistical methods in bioinformatics: an introduction | 2005 |
19079 | Mitchell, Tom M. | Machine learning | 2005 |
19080 | Grimmett, Geoffrey | Probability and random processes | 2005 |
19083 | Hinton, Geoffrey | Unsupervised learning: foundations of neural computation | 1999 |
19084 | Weigend, Andreas S. | Time series prediction : forecasting the future and understanding the past | 1994 |
19085 | Barnett, Vic | Outliers in statistical data | 1998 |
19102 | Fall, Christopher P. | Computational cell biology | 2005 |
19103 | Kohonen, Teuvo | Self-organizing maps | 2001 |
19104 | Huber, Peter J. | Robust statistics | 2004 |
19105 | Bourne, Philip E. | Structural bioinformatics | 2003 |
19106 | Gersho, Allen | Vector quantization and signal compression | 1992 |
19107 | Vapnik, Vladimir N. | The nature of statistical learning theory | 2000 |
19109 | Sternberg, Michael J. E. | Protein structure prediction: a practical approach | 2002 |
19110 | Flower, Darren R. | Drug design: cutting edge approaches | 2002 |
19127 | Lesk, Arthur M. | Introduction to bioinformatics | 2005 |
19134 | Percus, Jerome K. | Mathematics of genome analysis | 2002 |
19135 | Ripley, Brian D. | Pattern recognition and neural networks | 2005 |
19154 | Cerami, Ethan | XML for Bioinformatics | 2005 |
19157 | Pearl, Judea | Probabilistic reasoning in intelligent systems: networks of plausible inference | 1997 |
19160 | Waterman, Michael S. | Introduction to computational biology: maps, sequences and genomes | 2000 |
19161 | Efron, Bradley | An introduction to the bootstrap | 1998 |
19162 | Cox, Trevor F. | Multidimensional scaling | 2001 |
19172 | Koski, Timo | Hidden Markov models for bioinformatics | 2001 |
19173 | Alberts, Bruce | Molecular biology of the cell | 2002 |
19174 | Lodish, Harvey F. | Molecular cell biology | 2004 |
19234 | Lesk, Arthur M. | Database annotation in molecular biology | 2005 |
19284 | Campbell, A. Malcolm | Discovering genomics, proteomics, and bioinformatics | 2007 |
19285 | Mount, David W. | Bioinformatics: sequence and genome analysis | 2004 |
19290 | McLachlan, Geoffrey J. | Analyzing microarray gene expression data | 2004 |
19291 | Orengo, Christine A. | Bioinformatics: genes, proteins and computers | 2005 |
19292 | Speed, Terry | Statistical analysis of gene expression microarray data | 2003 |
19297 | Pennington, Stephen R. | Proteomics: from protein sequence to function | 2001 |
19308 | Claverie, Jean-Michel | Bioinformatics for dummies | 2006 |
19388 | Lidie, Stephen | Mastering Perl/Tk | 2002 |
19398 | Orwant, Jon | Mastering algorithms with Perl | 1999 |
19673 | Press, William H. | Numerical recipes: the art of scientific computing | 2007 |
19691 | Paradis, Emmanuel | Analysis of phylogenetics and evolution with R | 2006 |
19793 | Draper, Norman Richard | Applied regression analysis | 1998 |
19794 | Hosmer, David W. | Applied logistic regression | 2000 |
19868 | Dudoit, Sandrine | Multiple testing procedures with applications to genomics | 2008 |
19869 | Gentleman, Robert | Bioinformatics and computational biology solutions using R and Bioconductor | 2005 |
19979 | Semizarov, Dimitri | Genomics in drug discovery and development | 2009 |
20010 | Boyd, Stephen P. | Convex optimization | 2008 |
20019 | Bishop, Christopher M. | Pattern recognition and machine learning | 2006 |
20142 | Ligges, Uwe | Programmieren mit R | 2008 |
20148 | Chambers, John M. | Software for data analysis: programming with R | 2008 |
20149 | Gentleman, Robert | R programming for bioinformatics | 2009 |
20150 | Murrell, Paul | R graphics | 2006 |
20157 | Leach, Andrew R. | An introduction to chemoinformatics | 2007 |
20187 | Hartl, Daniel L. | Principles of population genetics | 2007 |
20188 | Foulkes, Andrea S. | Applied statistical genetics with R: for population-based association studies | 2009 |
20189 | Wickham, Hadley | ggplot2: elegant graphics for data analysis | 2009 |
20190 | Broman, Karl W. | A guide to QTL mapping with R/qtl | 2009 |
20191 | Claude, Julien | Morphometrics with R | 2008 |
20213 | Hartl, Daniel L. | A primer of population genetics | 2000 |
20216 | Siegmund, David | The statistics of gene mapping | 2007 |
20217 | Gustafson, J. Perry | Genomics of Disease | 2008 |
20218 | Nielsen, Kare Lehmann | Serial analysis of gene expression (SAGE) | 2008 |
20219 | Maly, Ivan V. | Systems biology | 2009 |
20220 | Sarkar, Deepayan | Lattice: multivariate data visualization with R | 2008 |
20221 | Maindonald, John | Data analysis and graphics using R: an example-based approach | 2008 |
20228 | Bolker, Benjamin M. | Ecological models and data in R | 2008 |
20231 | Falus, Andras | Clinical applications of immunomics | 2009 |
20232 | Forsdyke, Donald R. | Evolutionary Bioinformatics | 2006 |
20233 | Hahne, Florian | Bioconductor case studies | 2008 |
20234 | Isaev, Alexander | Introduction to mathematical methods in bioinformatics | 2006 |
20235 | Murphy, William J. | Phylogenomics | 2008 |
20236 | Krawetz, Stephen A. | Bioinformatics for systems biology | 2009 |
20237 | Chapelle, Olivier | Semi-supervised learning | 2006 |
20238 | Getoor, Lise | Introduction to statistical relational learning | 2007 |
20245 | Jaynes, Edwin T. | Probability theory: the logic of science | 2009 |
20253 | Lemey, Philippe | The phylogenetic handbook | 2009 |
20254 | Deonier, Richard C. | Computional genome analysis: an introduction | 2005 |
20255 | Chao, Kun-Mao | Sequence comparison: theory and methods | 2008 |
20269 | Gan, Guojun | Data clustering: theory, algorithms, and applications | 2007 |
20270 | Edelstein-Keshet, Leah | Mathematical models in biology | 2005 |
20271 | Cox, David R. | Principles of statistical inference | 2006 |
20272 | Cristianini, Nello | Introduction to computational genomics: a case studies approach | 2007 |
20273 | Do, Kim-Anh | Bayesian inference for gene expression and proteomics | 2006 |
20274 | Schölkopf, Bernhard | Kernel methods in computational biology | 2004 |
20275 | Fertin, Guillaume | Combinatorics of genome rearrangements | 2009 |
20276 | Hammerstein, Peter | Genetic and cultural evolution of cooperation | 2003 |
20277 | Lund, Ole | Immunological bioinformatics | 2005 |
20281 | Hofbauer, Josef | Evolutionary games and population dynamics | 2003 |
20282 | Leonard, Thomas | Bayesian methods: an analysis for statisticians and interdisciplinary researchers | 2009 |
20288 | Koller, Daphne | Probabilistic graphical models: principles and techniques | 2009 |
20289 | Bürger, Reinhard | The mathematical theory of selection, recombination, and mutation | 2000 |
20290 | Hamilton, Matthew B. | Population genetics | 2009 |
20291 | Gasteiger, Johann | Chemoinformatics: a textbook | 2008 |
20304 | Kogan, Jacob | ntroduction to clustering large and high-dimensional data | 2007 |
20305 | Avise, John C. | Evolutionary pathways in nature: a phylogenetic approach | 2008 |
20308 | Bottou, Léon | Large-scale Kernel machines | 2007 |
20329 | Freedman, David | Statistical models: theory and practice | 2009 |
20342 | Jacoby, Edgar | Chemogenomics: knowledge-based approaches to drug discovery | 2006 |
20418 | McLachlan, Geoffrey J. | The EM algorithm and extensions | 2008 |
20500 | Barnes, Michael R. | Genetic variation: methods and protocols | 2010 |
20503 | Moorhouse, Michael | Bioinformatics biocomputing and Perl: an introduction to bioinformatics, computing skills and practice | 2004 |
20505 | Motulsky, Harvey | Intuitive biostatistics | 1995 |
20509 | Greene, Judith | Learning to use statistical tests in psychology | 2006 |
20513 | Klipp, Edda | Systems biology: a textbook | 2009 |
20514 | Gruijter, Dato N. M. de | Statistical test theory for the behavioral sciences | 2008 |
20517 | Tisdall, James D. | Mastering Perl for bioinformatics | 2003 |
20527 | Kanji, Gopal K. | 100 statistical tests | 2006 |
20538 | Miller, Frederic P. | Copy number variation: DNA, Genome, Ploidy, Deletion (genetics), Gene duplication, Low copy repeats, Segmental duplication, Cytogenetics, Fluorescent ... Comparative genomic hybridization | 2010 |
20664 | Laird, Nan M. | The Fundamentals of Modern Statistical Genetics | 2011 |
20709.1 | Balding, David J. | Handbook of Statictical Genetics | 2007 |
20709.2 | Balding, David J. | Handbook of Statictical Genetics | 2007 |
21461 | Neale, Benjamin | Statistical Genetics: Gene Mapping Through Linkage and Association | 2008 |
21551 | Barillot, Emmanuel | Computational Systems Biology of Cancer | 2013 |
21558 | Rencher, Alvin C. | Linear models in statistics | 2008 |
21565 | Murphy, Kevin P. | Machine learning: A Probabilistic Perspective | 2012 |
21576 | Moore, David S. Moore | Introduction to the practice of statistics | 2012 |
21644 | McKinney, Wes | Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython | 2012 |
21682 | Sanders, Jason | CUDA by Example: An Introduction to General-Purpose GPU Programming | 2011 |
21683 | Reed, David | Chemistry for Biologists | 2013 |
21686 | Lutz, Mark | Learning Python | 2013 |
21687 | Wilt, Nickolas | The CUDA Handbook | 2013 |
21690 | Paradis, Emmanuel | Analysis of Phylogenetics and Evolution with R (2nd Edition) | 2012 |
21692 | Richert, Willi et. al | Building Machine Learning Systems with Python | 2013 |
21742 | Eddelbuettel, Dirk | Seamless R and C++ Integration with Rcpp | 2013 |
21743 | Lütkepohl, Helmut | Handbook of matrices | 1996 |
21774 | Deng, Li | Deep Learning Methods and Applications |
2014 |
21843 | Manning, Christopher D. et al. | Foundations of Statistical Natural Language Processing | 1999 |
21844 | Szeliski, Richard | Computer Vision: Algorithms and Applications | 2011 |
21847 | Kerber, Adalbert et al. | Mathematical Chemistry and Chemoinformatics | 2014 |
21912 | Goodfellow, Ian et al. | Deep Learning | 2016 |
21913 | Kushner, Harald J. et al. | Stochastic Approximation and Recursive Algorithms and Applications | 2003 |
21914 | Dayan, Peter et al. | Theoretical Neuroscience | 2001 |
22077 | Sutton, Richard S. and Barto, Andrew G. | Reinforcement Learning: An Introduction (2nd Edition) | 2018 |
22079 | Nocedal, Jorge and Wright, Stephen J. | Numerical Optimization | 2006 |
22080 | Casella, George and Berger, Roger L. | Statistical Inference | 2002 |
22081 | Pearl, Judea | Causality | 2019 |
22107 | Bertsekas, Dimitri P. | Reinforcement Learning and Optimal Control | 2019 |
22113 | Peters, Jonas et al. | Elements of Causal Inference | 2017 |
22131 | Zhou, S. Kevin et al. | Deep learning for medical image analysis | 2017 |
22169 | Vershynin, Roman et al. | High-dimensional probability : an introduction with applications in data science | 2018 |
22175 | Nemeth, Evi et al. | Unix and Linux system administration handbook | 2018 |
22177 | Brown, Nathan | Artificial intelligence in drug discovery | 2021 |
22178 | Rudin, Walter | Functional analysis | 1991 |
22256 | Wainwright, Martin J. | High-Dimensional Statistics | 2019 |
22311 | Bernstein, Dennis S. | Matrix Mathematics | 2009 |
22325 | William Strunk Jr | The Elements of Style | |
22326 | Robert C. Martin | Clean Code: A Handbook of Agile Software Craftsmanship | |
22331 | Shai Shalev-Shwartz | Understanding Machine Learning: From Theory to Algorithms | |
22332 | Steven L. Brunton, J. Nathan Kutz | Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control |