Thomas, M. T. C. A. J., and A. Thomas Joy. Elements of information theory. Wiley-Interscience, 2006.
Video lecture: Information theory
Theoretic Textbook: Boucheron S, Lugosi G, Massart P. Concentration inequalities: A nonasymptotic theory of independence[M]. Oxford university press, 2013.
Applied Textbook: Vershynin R. High-dimensional probability: An introduction with applications in data science[M]. Cambridge University Press, 2018.
Courses/Video lectures: High dimensional probability
Cookbook: Petersen K B, Pedersen M S. The matrix cookbook[J]. Technical University of Denmark, 2008, 7(15): 510.
Theoretic Textbook: Horn R A, Horn R A, Johnson C R. Matrix analysis[M]. Cambridge university press, 1990.
Computational Textbook: Golub G H, Van Loan C F. Matrix computations[M]. JHU Press, 2012.
Courses/Video lectures: Matrix analysis
H. Lee. NTU - Linear algebra: 2023F
Cormen TH, Leiserson CE, Rivest RL, Stein C. Introduction to algorithms. MIT press; 2022 Apr 5.
Kleinberg J, Tardos E. Algorithm design. Pearson Education India; 2006.
Courses/Video lectures: Introduction to algorithms
Applied Textbook: Boyd S, Vandenberghe L. Convex optimization[M]. Cambridge university press, 2004.
Fundamental Textbook: Nocedal J, Wright S J. Numerical optimization[M]. Springer, 1999.
Courses/Video lectures: Convex optimization
Applied Textbook: Theodoridis, Sergios. Machine learning: a Bayesian and optimization perspective. Academic press, 2015.
Theoretical Textbook: Shalev-Shwartz, Shai, and Shai Ben-David. Understanding machine learning: From theory to algorithms. Cambridge university press, 2014.
Courses/Video lectures: Understanding machine learning, Machine learning
Applied Textbook: Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.
Anthony M, Bartlett PL, Bartlett PL. Neural network learning: Theoretical foundations. Cambridge: cambridge university press; 1999 Nov 4.
Courses/Video lectures: Foundations of deep learning, CNN for computer vision 2022S
Practical Textbook: Solomon C, Breckon T. Fundamentals of Digital Image Processing: A practical approach with examples in Matlab. John Wiley & Sons; 2011.
Fundamental Textbook: Sonka M, Hlavac V, Boyle R. Image processing, analysis and machine vision. Springer; 2013 Nov 11.
Courses/Video lectures: Introduction to image processing and computer vision
Theoretic Textbook: Rauhut, Holger, and Foucart Simon. A mathematical introduction to compressive sensing. Springer, 2012.
Applied Textbook: Eldar Y, Kutyniok G. Compressed Sensing: Theory and Applications 1st Edition. Cambridge University Press, 2012.
Courses/Video lectures: Compressed sensing and sparse recovery
Breast Imaging Textbook: Berg WA, Leung JW. Diagnostic Imaging: Breast E-Book. Elsevier Health Sciences; 2019 Jun 17.
BIRADS: ACR. BI-RADS Atlas 5th Edition. American College of Radiology, 2013.
Cardiac Imaging Textbook: Heller, G, Hendel R. Nuclear Cardiology: Practical Applications, Fourth Edition. McGraw Hill / Medical, 2022.
Klenke A. Probability theory: a comprehensive course[M]. Springer Science & Business Media, 2013.
Kallenberg O. Foundations of modern probability[M]. Springer Science & Business Media, 2006.
Tao T. Topics in random matrix theory[M]. American Mathematical Soc., 2012.
Akemann G, Baik J, Di Francesco P. The Oxford handbook of random matrix theory[M]. Oxford University Press, 2011.
Ledoux M. The concentration of measure phenomenon[M]. American Mathematical Soc., 2001.
an Handel R. Probability in high dimension[R]. PRINCETON UNIV NJ, 2014.
Anderson G W, Guionnet A, Zeitouni O. An introduction to random matrices[M]. Cambridge university press, 2010.
Cox D R. The theory of stochastic processes[M]. Routledge, 2017.
MATH 581: High Dimensional Probability and Statistical Learning @ UW
ECE 18-898G: Special Topics in Signal Processing: Sparsity, Structure, and Inference @ CMU
Topics in Theoretical Computer Science: An Algorithmist's Toolkit @ MIT
Bhatia R. Matrix analysis[M]. Springer Science & Business Media, 2013.
Bodewig E. Matrix calculus[M]. Elsevier, 2014.
Stewart G W, Sun J. Matrix perturbation theory[M]. Academic Press, Inc. 1990.
Nesterov Y. Introductory lectures on convex optimization: A basic course[M]. Springer Science & Business Media, 2013.
Rockafellar R T. Convex analysis[M]. Princeton university press, 2015.
EE364a: Convex Optimization I (Video available)
EE364b - Convex Optimization II (Video available)
EE 150: Applications of Convex Optimization in Signal Processing and Communications
Lectures by Prof. Hungyi Lee: machine learning 2015F, machine learning 2016F, machine learning 2017S, machine learning 2017S-2, machine learning 2017F, machine learning 2018S, machine learning 2019S, machine learning 2020S, machine learning 2021S, machine learning 2022S, deep learning language processing 2022S
Lectures by Prof. Fei-Fei Li: CNN for computer vision 2015W, CNN for computer vision 2016W, CNN for computer vision 2017F, CNN for computer vision 2018S, CNN for computer vision 2019S, CNN for computer vision 2020S, CNN for computer vision 2021S, CNN for computer vision 2022S
Lectures by Lex Fridman: deep learning and artificial intelligence 2017-2020
Lectures by: intro to deep learning 2018-2022
Mohri, Mehryar, Afshin Rostamizadeh, and Ameet Talwalkar. Foundations of machine learning. MIT press, 2018.