CSCI1420 & ENGN2520 course syllabus (pdf) and grading policies (pdf).
Previous courses: Spring 2015, Fall 2013, Spring 2013, Spring 2012, Spring 2011, Fall 2009.

Online References

• The Elements of Statistical Learning. T. Hastie, R. Tibshirani, & J. Friedman, Springer, 2009.
• Bayesian Reasoning and Machine Learning. D. Barber, Cambridge University Press, 2012.
• Information Theory, Inference, and Learning Algorithms. D. MacKay, Cambridge University Press, 2003.
• Convex Optimization. S. Boyd & L. Vandenberghe, Cambridge University Press, 2004.
• Probabilistic Graphical Models

Print Textbooks

• Pattern Recognition and Machine Learning. C. Bishop, Springer, 2007.
• Machine Learning: A Probabilistic Perspective. K. Murphy, MIT Press, 2012.
• Pattern Classification. R. Duda, P. Hart, & D. Stork, Wiley, 2001.
• Machine Learning. T. Mitchell, McGraw Hill, 1997.
• Introduction to Probability. D. Bertsekas & J. Tsitsiklis, Athena Scientific, 2008.
• Bayesian Data Analysis. A. Gelman, J. Carlin, H. Stern, & D. Rubin, CRC Press, 2013.

Machine Learning Software

• YAGTOM: Yet Another Guide TO Matlab.
• Matlab Machine Learning Toolboxes. Especially the PMTK, Kevin Murphy, Google.
• mloss.org. Machine learning open source software.
• Weka. Data mining software in Java.
• Lightspeed Matlab Toolbox. Tom Minka, Microsoft Research.

LaTeX

• Wikibooks LaTeX Manual, especially the sections on mathematics, lists, importing graphics, and floats and figures.
• LaTeX tutorial videos prepared by Joe DiNoto.
• Texmaker and Kile are LaTeX editors available on department workstations, and via most linux package managers.
• MacTeX and MiKTeX make it easy to install integrated LaTeX environments on your Mac or Windows computer.