

Click the serial number on the left to view the details of the item. 
# 
Author  Title  Accn#  Year  Item Type  Claims 
1 
Sch??tt, Kristof T 
Machine Learning Meets Quantum Physics 
I09716 
2020 
eBook 

2 
Helias, Moritz 
Statistical Field Theory for Neural Networks 
I09583 
2020 
eBook 

3 
Czischek, Stefanie 
NeuralNetwork Simulation of Strongly Correlated Quantum Systems 
I09120 
2020 
eBook 

4 
Zhao, Haitao 
Feature Learning and Understanding 
I09105 
2020 
eBook 

5 
Stark, Giordon 
The Search for Supersymmetry in Hadronic Final States Using Boosted Object Reconstruction 
I09004 
2020 
eBook 

6 
Matthew Kirk 
Thoughtful machine learning: A TestDriven Aproach 
026224 
2014 
Book 

7 
Nikhil Buduma 
Fundamentals of deep learning: designing next generation machine intelligence algorithms 
PP0025 

Book 

8 
Christopher M. Bishop 
Pattern recognition and machine learning 
026213 
2011 
Book 

9 
Ian Goodfellow 
Deep learning 
026211 
2017 
Book 

10 
Yuichiro Anzai 
Pattern recognition and machine learning 
008160 
1992 
Book 


1.


Title  Machine Learning Meets Quantum Physics 
Author(s)  Sch??tt, Kristof T;Chmiela, Stefan;von Lilienfeld, O. Anatole;Tkatchenko, Alexandre;Tsuda, Koji;M??ller, KlausRobert 
Publication  Cham, 1. Imprint: Springer
2. Springer International Publishing, 2020. 
Description  XVI, 467 p. 137 illus., 125 illus. in color : online resource 
Abstract Note  Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate firstprinciples calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long timescales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current stateoftheart are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context. 
ISBN,Price  9783030402457 
Keyword(s)  1. Chemistry, Physical and theoretical
2. EBOOK
3. EBOOK  SPRINGER
4. MACHINE LEARNING
5. Numerical and Computational Physics, Simulation
6. PHYSICS
7. QUANTUM PHYSICS
8. Theoretical and Computational Chemistry

Item Type  eBook 
MultiMedia Links
Please Click here for eBook
Circulation Data
Accession#  
Call#  Status  Issued To  Return Due On  Physical Location 
I09716 


On Shelf 




2.


Title  Statistical Field Theory for Neural Networks 
Author(s)  Helias, Moritz;Dahmen, David 
Publication  Cham, 1. Imprint: Springer
2. Springer International Publishing, 2020. 
Description  XVII, 203 p. 127 illus., 5 illus. in color : online resource 
Abstract Note  This book presents a selfcontained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks. These powerful analytical techniques, which are well established in other fields of physics, are the basis of current developments and offer solutions to pressing open problems in theoretical neuroscience and also machine learning. They enable a systematic and quantitative understanding of the dynamics in recurrent and stochastic neuronal networks. This book is intended for physicists, mathematicians, and computer scientists and it is designed for selfstudy by researchers who want to enter the field or as the main text for a one semester course at advanced undergraduate or graduate level. The theoretical concepts presented in this book are systematically developed from the very beginning, which only requires basic knowledge of analysis and linear algebra 
ISBN,Price  9783030464448 
Keyword(s)  1. EBOOK
2. EBOOK  SPRINGER
3. MACHINE LEARNING
4. Mathematical Models of Cognitive Processes and Neural Networks
5. MATHEMATICAL STATISTICS
6. Neural networks (Computer science)??
7. Neurosciences
8. Probability and Statistics in Computer Science
9. STATISTICAL PHYSICS
10. Statistical Physics and Dynamical Systems

Item Type  eBook 
MultiMedia Links
Please Click here for eBook
Circulation Data
Accession#  
Call#  Status  Issued To  Return Due On  Physical Location 
I09583 


On Shelf 




3.


Title  NeuralNetwork Simulation of Strongly Correlated Quantum Systems 
Author(s)  Czischek, Stefanie 
Publication  Cham, 1. Imprint: Springer
2. Springer International Publishing, 2020. 
Description  XV, 205 p. 51 illus., 48 illus. in color : online resource 
Abstract Note  Quantum systems with many degrees of freedom are inherently difficult to describe and simulate quantitatively. The space of possible states is, in general, exponentially large in the number of degrees of freedom such as the number of particles it contains. Standard digital highperformance computing is generally too weak to capture all the necessary details, such that alternative quantum simulation devices have been proposed as a solution. Artificial neural networks, with their high nonlocal connectivity between the neuron degrees of freedom, may soon gain importance in simulating static and dynamical behavior of quantum systems. Particularly promising candidates are neuromorphic realizations based on analog electronic circuits which are being developed to capture, e.g., the functioning of biologically relevant networks. In turn, such neuromorphic systems may be used to measure and control real quantum manybody systems online. This thesis lays an important foundation for the realization of quantum simulations by means of neuromorphic hardware, for using quantum physics as an input to classical neural nets and, in turn, for using network results to be fed back to quantum systems. The necessary foundations on both sides, quantum physics and artificial neural networks, are described, providing a valuable reference for researchers from these different communities who need to understand the foundations of both 
ISBN,Price  9783030527150 
Keyword(s)  1. CONDENSED MATTER
2. CONDENSED MATTER PHYSICS
3. EBOOK
4. EBOOK  SPRINGER
5. MACHINE LEARNING
6. Mathematical Models of Cognitive Processes and Neural Networks
7. Neural networks (Computer science)??
8. QUANTUM PHYSICS

Item Type  eBook 
MultiMedia Links
Please Click here for eBook
Circulation Data
Accession#  
Call#  Status  Issued To  Return Due On  Physical Location 
I09120 


On Shelf 




4.


Title  Feature Learning and Understanding : Algorithms and Applications 
Author(s)  Zhao, Haitao;Lai, Zhihui;Leung, Henry;Zhang, Xianyi 
Publication  Cham, 1. Imprint: Springer
2. Springer International Publishing, 2020. 
Description  XIV, 291 p. 126 illus., 109 illus. in color : online resource 
Abstract Note  This book covers the essential concepts and strategies within traditional and cuttingedge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometricalstructurebased methods, but also advanced feature learning methods, such as sparse learning, lowrank decomposition, tensorbased feature extraction, and deeplearningbased feature learning. Each feature learning method has its own dedicated chapter that explains how it is theoretically derived and shows how it is implemented for realworld applications. Detailed illustrated figures are included for better understanding. This book can be used by students, researchers, and engineers looking for a reference guide for popular methods of feature learning and machine intelligence 
ISBN,Price  9783030407940 
Keyword(s)  1. Computational Intelligence
2. Datadriven Science, Modeling and Theory Building
3. EBOOK
4. EBOOK  SPRINGER
5. ECONOPHYSICS
6. IMAGE PROCESSING
7. Image Processing and Computer Vision
8. MACHINE LEARNING
9. OPTICAL DATA PROCESSING
10. PATTERN RECOGNITION
11. SIGNAL PROCESSING
12. Signal, Image and Speech Processing
13. Sociophysics
14. Speech processing systems

Item Type  eBook 
MultiMedia Links
Please Click here for eBook
Circulation Data
Accession#  
Call#  Status  Issued To  Return Due On  Physical Location 
I09105 


On Shelf 




5.


Title  The Search for Supersymmetry in Hadronic Final States Using Boosted Object Reconstruction 
Author(s)  Stark, Giordon 
Publication  Cham, 1. Imprint: Springer
2. Springer International Publishing, 2020. 
Description  XVI, 257 p. 150 illus., 140 illus. in color : online resource 
Abstract Note  This thesis represents one of the most comprehensive and indepth studies of the use of Lorentzboosted hadronic final state systems in the search for signals of Supersymmetry conducted to date at the Large Hadron Collider. A thorough assessment is performed of the observables that provide enhanced sensitivity to new physics signals otherwise hidden under an enormous background of top quark pairs produced by Standard Model processes. This is complemented by an ingenious analysis optimization procedure that allowed for extending the reach of this analysis by hundreds of GeV in mass of these hypothetical new particles. Lastly, the combination of both deep, thoughtful physics analysis with the development of highspeed electronics for identifying and selecting these same objects is not only unique, but also revolutionary. The Global Feature Extraction system that the author played a critical role in bringing to fruition represents the first dedicated hardware device for selecting these Lorentzboosted hadronic systems in realtime using stateoftheart processing chips and embedded systems 
ISBN,Price  9783030345488 
Keyword(s)  1. EBOOK
2. EBOOK  SPRINGER
3. Elementary particles (Physics)
4. Elementary Particles, Quantum Field Theory
5. MACHINE LEARNING
6. Measurement Science and Instrumentation
7. Measurement??????
8. Particle acceleration
9. Particle Acceleration and Detection, Beam Physics
10. PHYSICAL MEASUREMENTS
11. QUANTUM FIELD THEORY

Item Type  eBook 
MultiMedia Links
Please Click here for eBook
Circulation Data
Accession#  
Call#  Status  Issued To  Return Due On  Physical Location 
I09004 


On Shelf 



 