Research Books

*** Statistics, Probability ***

1. Statistics for Business and Economics 11th ed (2011 Book)

2. Applied Statistics in Business and Economics

3. Applied Statistics and Probability for Engineers, 6th Edition (by Montgomery and Runger)

 *** Math Methods in Science and Engineering ***

1. Essentials of Mathematical Methods in Science and Engineering

2. Handbook-of-math-for-engineers-and-scientists

3. Mathematical Methods for Physics and Engineering

4. Advanced Mathematical Methods for Scientists and Engineers (by Sean Mauch, 2003)

  *** Calculus ***

1. 微積分學 (2014)

2. Calculus -Early Transcendentals 7th txtbk - Stewart

3. Calculus Early Transcendentals, 10th Edition  (*** Instructor's Solutions Manual)

4. 3000 Solved Problems in Calculus

5. Calculus, Volume 1 (2016, by Gilbert Strang, Edwin “Jed” Herman)

6. Calculus, Volume 2 (2017, by Gilbert Strang, Edwin “Jed” Herman)

7. Calculus, Volume 3 (2018, by Gilbert Strang, Edwin “Jed” Herman)

 *** Calculus of Variations (變分法) ***

1. A first course in the calculus of variations (by Mark Kot, 2014)

2. The Calculus of Variations (by Bruce van Brunt, 2004)

3. The Calculus of Variations (by M. Bendersky, 2008)

4. Advanced engineering analysis _ the calculus of variations and functional analysis with applications in mechanics

*** Engineering Mathematics ***

1. Advanced engineering mathematics, KREYSZIG, 10th ed

*** Linear Algebra ***

1. Introduction to Linear Algebra (5th ed, 2016)(by G. Strang)

2. Introduction to Linear Algebra (4th ed, 2009)(by G. Strang)

3. Linear Algebra and Learning from Data (2019, Gilbert Strang).pdf

4. Linear Algebra with Applications (8th ed., 2012)(Gareth Williams)

5. Linear Algebra with Applications (9th ed., 2017)(Gareth Williams)

6. A Concise Introduction to Linear Algebra(2012)(Gaza Schay)

7. Elementary linear algebra (11th_edition. 2014)(by Howard Anton and Chris Rorres)

8. Elementary linear algebra (10th edition, 2010) ( by H.Anton and C. Rorres)

9. Essential Linear Algebra with Applications_ A Problem-Solving Approach (by Titu Andreescu, 2014)

10. Numerical Linear Algebra_ A Concise Introduction with MATLAB and Julia (by Folkmar Bornemann, Translated by Walter Simson, Munich, Germany)(2018)

11. Linear Algebra_ A Modern Introduction (by David Poole, 4th ed., 2015)

12. Basics for Linear Algebra for Machine Learning - Discover the Mathematical Language of Data in Python (by Jason Brownlee, 2018)

13. Applied Linear Algebra (by Peter J. Olver • Chehrzad Shakiban, 2nd ed., 2018 )

14. Linear Algebra (2nd ed., 2007)(Bronson and Costa)

15. Linear_Algebra (4th_Ed., 2009) (Lipschutz-Lipson)

16. Linear Algebra and Linear Operators in Engineering_ With Applications in Mathematica ( 2000, by  H. Ted Davis and Kendall T. Thomson)

17. Handbook of Linear Algebra, 2nd Ed (Edited by Leslie Hogben, 2014)

      *** Matrix Theory ***

1. Matrix Mathematics_ Theory, Facts, and Formulas_ 2nd Ed (Dennis S. Bernstein, 2009)

2. Matrix Computations, 4th Ed (Gene H. Golub & C.F. Van Loan, 2013)  

3. Matrix Algebra_ Theory, Computations and Applications in Statistics, 2nd Ed (James E. Gentle, 2017)

4. Matrix Theory_ From Generalized Inverses to Jordan Form (2007, by Robert Piziak and  P. L. Odell)

5. Matrix Algebra_ Theory, Computations and Applications in Statistics (by James E. Gentle , 2nd ed.,  2017)

       *** Singular Value Decomposition ***

1. Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition (2011, by Haruo Yanai, Kei Takeuchi, and Yoshio Takane)

2. Linear Algebra for Pattern Processing_ Projection, Singular Value Decomposition, and Pseudoinverse (2021, by Kenichi Kanatani)

3. Analysis and Linear Algebra_ The Singular Value Decomposition and Applications-American Mathematical Society (2021, by James Bisgard)

4. Singular Value Decomposition for High Dimensional Data (2012, Dissertation, by Dan Yang)

       *** Principal Component Analysis ***

1. Generalized Principal Component Analysis (2016, by René Vidal • Yi Ma • S. Shankar Sastry)

2. Principal Component Analysis (Jolliffe I. , 2ed., Springer, 2002)

3. Hotelling (1933) Paper: 

• ANALYSIS OF A COMPLEX OF STATISTICAL VARIABLES INTO PRINCIPAL COMPONENTS (1 of 2) (by Hotelling, 1933)
• ANALYSIS OF A COMPLEX OF STATISTICAL VARIABLES INTO PRINCIPAL COMPONENTS (2 of 2) (by Hotelling, 1933)

4. Principal Component Analysis (2012, Edited by Parinya Sanguansat)

5. Principal Component Analysis Networks and Algorithms (2017, by Duan, Zhansheng_Hu, Changhua_Kong, Xiangyu)

6. Principal component analysis in meteorology and oceanography (1988, by Rudolph W Preisendorfer)

       *** Multivariate Analysis ***

1. Modern Multivariate Statistical Techniques_ Regression, Classification, and Manifold Learning (2008, by Alan J. Izenman)

2. Applied Multivariate Statistical Concepts (2017, by Debbie L. Hahs-Vaughn)

3. Applied Multivariate Statistics for the Social Sciences_ Analyses with SAS and IBM's SPSS (2016, by Keenan A. Pituch and James P. Stevens)

4. Applied Multivariate Analysis (2002, by Neil H. Timm)

5. An Introduction to Applied Multivariate Analysis with R (2011, by Brian Everitt • Torsten Hothorn)

*** Linear Programming ***

1. Linear Programming and Extensions-Princeton University Press (1963) -by  George Dantzig

2. Applied Linear Programming. Academic Press (1978) __ by Michael R. Greenberg

       *** Discrete Mathematics ***

1. Discrete Mathematics with Applications ( 2020, 5th Ed., by SUSANNA S. EPP)

2. Discrete mathematics and its applications (2019,  8th Ed., Kenneth H. Rosen)

3. Discrete Mathematics for Computer Science

4. Mathematical structures for computer science _ discrete mathematics and its applications

5. A Beginner's Guide to Discrete Mathematics (2012, 2nd Ed., W.D. Wallis)

*** Algorithms ***

1. Introduction to Algorithms (2009, 3rd Ed., by Thomas H. Cormen Charles E. Leiserson Ronald L. Rivest Clifford Stein, MIT Press)

*** Numerical Methods ***

1. APPLIED NUMERICAL METHODS USING MATLAB

2. Applied Numerical Methods with MATLAB for Engineers and Scientists (4th ed)

3. Applied Numerical Methods with MATLAB  for Engineers Scientists (3rd Ed) 

*** Quantum Physics  & Relativity Theory***

1. Relativity The Special and the General Theory (Albert Einstein, Roger Penrose, Robert Geroch etc., 2005)

2. Principles of Quantum Mechanics (by P.A.M. Dirac, 1967)

3. Mathematical Foundations of Quantum Mechanics (JOHN v.oN NEUMANN, 1955)

4. Introduction to Quantum Mechanics (3th Edition)

5. Quantum Mechanics_A Modern and Concise Introductory Course

6. Quantum physics _ what everyone needs to know

7. Quantum Mechanics_ Theory and Applications

8. Quantum Concepts in Physics_ An Alternative Approach to the Understanding of Quantum Mechanics

9. Quantum Mechanics – Concepts and Applications

10. Quantum mechanics with applications to nanotechnology and information science

11. Mathematical Methods in Physics_ Distributions, Hilbert Space Operators, Variational Methods, and Applications in Quantum Physics

12. Spectral Theory and Quantum Mechanics_ Mathematical Foundations of Quantum Theories, Symmetries and Introduction to the Algebraic Formulation (2nd Ed.)

13. Fundamentals of Quantum Mechanics (Ajit Kumar, 2018)

*** Quantum Information, Quantum Computing ***

1. Quantum Computation and Quantum Information (Nielsen & Isaac L. Chuang, 2010)

2. Quantum mechanics with applications to nanotechnology and information science (Band and Avishai, 2013)

3. The Second Quantum Revolution_ From Entanglement to Quantum Computing and Other Super-Technologies ( Lars Jaeger, 2018 )

4. The Physics of Quantum Information_ Quantum Cryptography, Quantum Teleportation, Quantum Computation

5. Quantum Computation and Logic

6. Quantum Physics for Scientists and Technologists_ Fundamental Principles and Applications for Biologists, Chemists, Computer Scientists, and Nanotechnologists

7. Introduction to Topological Quantum Matter & Quantum Computation (Tudor D. Stanescu, 2017)

*** Medicine, Brain, Bio-related Science *** 

1. Atlas of Human Anatomy

2. Eduard_Pernkopf_Atlas_of_Topographical_and_Ap (1)_text

*** Ill-posted Problems ***

1. Numerical Methods for the Solution of Ill-Posed Problems (1995, by A. N. Tikhonov, A. V. Goncharsky, V. V. Stepanov, A. G. Yagola)

2. Iterative regularization methods for nonlinear ill-posed problems (2008, by Kaltenbacher, Barbara)

3. Regularization Theory for Ill-posed Problems Selected Topics (2013, by Lu S., Pereverzev S.V.)

***Fuzzy, Neural, Neuro-Fuzzy, Machine Learning ***

1. Neural Networks Design_2nd ed_(2014, by Hagan et al.)

2. Mathematics for Machine Learning

3. Neuro-Fuzzy-and-Soft-Computing (1997, by Jyh-Shing Roger Jang, et al., searchable)

4. Neuro-Fuzzy-and-Soft-Computing (1997, unsearchable)

5. Neural Fuzzy Systems A Neuro-Fuzzy Synergism to Intelligent Systems (1996, by Chin-Teng Lin, C. S. George Lee)

6. Fuzzy Modeling And Fuzzy Control (2006,  by Huaguang Zhang, Derong Liu)

7. Introduction To Type-2 Fuzzy Logic Control Theory and Applications (2014, by Jerry Mendel, Hani Hagras, Woei-Wan Tan, William W. Melek, Hao Ying)

8. FUZZY SETS AND FUZZY LOGIC Theory and Applications (Book by GEORGE J. KLIR , BO YUAN)

9. Handbook of brain theory and neural networks (2002, by Michael A. Arbib, The MIT Press)

10. Fuzzy Logic for Business, Finance, and Management 

11. FUZZY LOGIC WITH ENGINEERING APPLICATIONS

12. Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems

13. A First Course in Fuzzy Logic _ 4th Ed. (2019, by Hung T. Nguyen, Carol L. Walker, Elbert A. Walker)

14. Fifty Years of Fuzzy Logic and its Applications (2015, by Dan E. Tamir, Naphtali D. Rishe, Abraham Kandel)

15. Learning_and_Soft_Computing - Support Vector Machines, Neural Networks, and Fuzzy Logic Models (Vojislav Kecman, 2001)

16. Neural Networks_ An Introduction

17. Neural Networks - A Comprehensive Foundation, 2nd Ed (by Simon Haykin)

18. Information theory, inference and learning algorithms ( by David J.C. MacKay, 2005)

19. Kalman Filtering and Neural Networks - (Simon Haykin ed., 2001)

20. Neural Networks_ Methodology and Applications ( by G. Dreyfus, 2005 )

21. Neural Networks_ Algorithms, Applications, and Programming Techniques (1991, James A. Freeman, David M. Skapura)

22. Principles of artificial neural networks (2007, Daniel Graupe)

23. The Elements of Statistical Learning_ Data Mining, Inference, and Prediction, 2nd Ed.  (by Trevor Hastie & Robert Tibshirani & Jerome Friedman, 2009)

24. NEURAL NETWORKS ( PPT in the pdf form ).pdf

*** Optimization  & Machine Learning***

1. Introduction to Non-Linear Optimization (1985, L. E. Scales)

2. Machine Learning_ A Probabilistic Perspective (2012, by Kevin P. Murphy, The MIT Press)

3. Foundations of Machine Learning (2018, by Mehryar Mohri_ Afshin Rostamizadeh_ Ameet Talwalkar,  The MIT Press)

4. An Introduction to Optimization

5. Advances in Applied Mathematics and Global Optimization_ In Honor of Gilbert Strang (2009, Gilbert Strang (auth.), David Y. Gao, Hanif D. Sherali (eds.))

6. Optimization modelling a practical approach (2008, by Ruhul Amin Sarker, Charles S. Newton)

7. Numerical Algorithms Methods for Computer Vision, Machine Learning, and Graphics (2015, by Justin Solomon)

8. Optimization Techniques in Computer Vision. Ill-posed Problems and Regularization (2016, by Mongi A. Abidi, Andrei V. Gribok, Joonki Paik)

*** Artificial Intelligence, Machine Learning***

1. Artificial Intelligence - A Modern Approach, 3rd ed (Stuart J. Russell & Peter Norvig)

2. 50 Years of Artificial Intelligence

3. Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 6th Ed.

4. Artificial Intelligence - A Guide to Intelligent Systems

5. Artificial intelligence (PPTs)

6. Artificial Intelligence By Example_ Develop machine intelligence from scratch using real artificial intelligence use cases

7. Artificial Intelligence for Big Data_ Complete guide to automating Big Data solutions using Artificial Intelligence techniques

8. Computer Science,Robotics,Artificial Intelligence,Neural Networks,IT

9. Encyclopedia-of-artificial-intelligence

*** Deep Learning ***

1. Deep Learning (2016, by Ian Goodfellow and Yoshua Bengio and Aaron Courville, the MIT Press)

2. Reinforcement Learning An Introduction, 2nd Edition  (2018, by Richard S. Sutton, Andrew G Barto, the MIT Press)

3. Deep Learning _ A Practical Approach

4. Deep learning_ adaptive computation and machine learning

5. Neural+Networks+and+Deep+Learning

6. Applied Deep Learning_ A Case-Based Approach to Understanding Deep Neural Networks

7. Introduction to Deep Learning Using R_ A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R

8. MATLAB Deep Learning_ With Machine Learning, Neural Networks and Artificial Intelligence

9. MIT 6.S191_ Introduction to Deep Learning

*** Data Science & Signal Processing ***

1. Adaptive Signal Processing (Widrow)

2. Digital Signal and Image Processing using MATLAB

3. Frontiers in Massive Data Analysis

4. System Identification_ Theory for the User (Ljung, 1999)

5. System Identification Toolbox (Matlab, 2019a)

6. System Identification-2011(Keesman)

7. Pattern Classification - CNS Classes

8. Pattern Recognition and Machine Learning - Springer  2006(Bishop)

9. Fundamentals of Machine Learning for Predictive Data Analytics_ Algorithms, Worked Examples, and Case Studies (J. D. Kelleher, B. M. Namee, A. D'Arcy, 2015 )

10. Data Mining (2015, by Charu C. Aggarwal)

11. Data Mining _ Practical Machine Learning Tools and Techniques, 3rd Ed (2011, by Ian H. Witten, Eibe Frank, Mark A. Hall)

12. Statistical and Machine-Learning Data Mining _ Techniques for Better Predictive Modeling and Analysis of Big Data, , 3rd Ed (2017, by Bruce Ratner)

13. Understanding Complex Datasets_ Data Mining with Matrix Decompositions (2007, by David Skillicorn) 

*** Electric and Microelectronic Circuits ***

1. Electric Circuits - 10th ed (2014, James W. Nilsson, Susan Riedel)

2. Fundamentals Of Electric Circuits, 5th-Edition (by Alexander and Sadiku)

3. Basic Electronics for Scientists and Engineers

4. Microelectronic Circuits, 7th Ed (by Sedra and Smith)

5. Microelectronics, 4th Ed (by Neamen)

*** Controls ***

1. Modern Control Engineering - 5th Ed  (2010, Katsuhiko Ogata)

2. Discrete-Time Control Systems - 2nd Ed (1995, Katsuhiko Ogata)

3. Modern Control Theory, 3rd Ed. (1991, by William L. Brogan)

   • Modern Control Theory, 3rd Ed. (1991, by William L. Brogan) (searchable version)

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*** Physics ***

1. Raymond A. Serway, John W. Jewett - Physics for Scientists and Engineers with Modern Physics-Cengage Learning (10th Ed, 2018) 

2. Randall Dewey Knight - Physics for Scientists and Engineers_ A Strategic Approach with Modern Physics (5th ED, 2021)

3. University Physics with Modern Physics with MasteringPhysics® (13th Edition, 2011  -by  Hugh D. Young, Roger A. Freedman, A. Lewis Ford)

4. Robert Resnick, David Halliday, Jearl Walker - Fundamentals of Physics (11th Ed, 2018)

5. Concepts of Modern Physics (6th ed, Beiser)

6. Elementary Particles (by Enrico Fermi)

7. Handbook of Physics

8. There's Plenty of Room at the Bottom(Richard P. Feynman,1960)

9. Modern classical physics_ optics, fluids, plasmas, elasticity, relativity, and statistical physics ( KIP S. THORNE and ROGER D. BLANDFORD, 2017)

10. College Physics

***Technical Analysis ***

1. The Handbook of Technical Analysis + Test Bank_ The Practitioner's Comprehensive Guide to Technical Analysis (2016, by MARK ANDREW LIM)