Good reading material for vision/learning research

Here is a listing of textbooks, tutorials and papers that I recommend for EE/CS grad students interested in computer vision and/or machine learning.

Introductory graduate level

Probability/stochastic processes:
G. Grimmett and D. Stirzaker, Probability and Random Processes, OUP, 2001
Very nicely written book, covering both basic and advanced material

Statistics:
L. Wasserman, All of Statistics, Springer, 2004
Overly simplified but well-written introductory book

Engineering mathematics:
G. Strang, Introduction to Applied Mathematics, Wellesley Cambridge Press, 1986
Non-rigorous, intuition-oriented introduction and review of many topics in applied maths

Analysis:
W. Rudin, Principles of Mathematical Analysis, 3rd ed., McGraw-Hill, 1976
A classic

Partial Differential Equations:
J. David Logan, Applied Partial Differential Equations, UTM, Springer, 1998
Good introduction for non-mathematics students

Linear Algebra:
R. Valenza, Linear algebra : An introduction to abstract mathematics, Springer-Verlag, 1993
Introduction to linear algebra that also introduces basic group theory

Machine learning/applied statistics:
D. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2003
Excellent textbook, presenting both information theory and statistical learning. The focus is on the underlying algorithms.
T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning, Springer, 2003
Well-written introduction. Includes relatively recent topics such as SVMs and boosting. Doesn't discuss graphical models.
M. Wainwright and M. Jordan, `Graphical models, exponential families, and variational inference,' Technical Report 649, Dept. of Statistics, UC Berkeley, 2003

Information theory:
T. Cover and J. Thomas, Elements of Information Theory, John Wiley, 1991
Still the best introduction to information theory. Useful concepts such as entropy, mutual information and KL divergence are discussed right at the beginning.

Optimisation:
D. Bertsekas, Nonlinear Programming, 2nd ed., Athena Scientific, 1999
D. Bertsekas, Convex Analysis and Optimization, Athena Scientific, 2003

Projective Geometry:
R. Hartley and A. Zisserman, Multiple View Geometry in computer vision, Cambridge Univ. Press, 2000

More advanced graduate material

W. Rudin, Real and Complex Analysis, 3rd ed., McGraw-Hill, 1986
Introduction to measure theory, which is the basis for advanced probability theory
M. Schervish, Theory of Statistics, Springer, 1996
Another classic yellow-book from Springer
A. Doucet, N. de Freitas and N. Gordon (eds.), Sequential Monte Carlo Methods in Practice, Springer-Verlag, 2001
S. Roweis and Z. Ghahramani, `A Unifying Review of Linear Gaussian Models,' Neural Computation, 11, 305-345, 1999
Excellent tutorial discussing a variety of models, including PCA, factor analysis, Gaussian mixture models, HMMs, Kalman filters and ICA


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