Computational Modeling and Artificial Intelligence (Cog Sci 109, 118A, 118B, 118C, 118D, 181, 185, 188, 189) 
 

An important early book: 

McClelland, J., & Rumelhart, D. (1988). Parallel distributed processing (Vols. 1 & 2). Cambridge: MIT Press.

Modern Books:

(Cogs 109) Modeling and Data Analysis
Richard Dude, Peter Hart, and David Stork, Pattern Classification (2nd edition), Wiley-Interscience

 (Cogs 118A) Introduction to Machine Learning I
Kevin Murphy, Adaptive Computation and Machine Learning : Machine Learning : A Probabilistic Perspective, MIT Press

(Cogs 118B) Introduction to Machine Learning II
Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer

(Cogs 118C) Neural Signal Processing
Wim van Drongelen, Signal Processing for Neuroscientists: An Introduction to the Analysis of Physiological Signals, Academic Press

(Cogs 118D) Mathematical Statistics for Behavioral Data Analysis
Mooris DeGroot and Mark Schervish, Probability and Statistics (4th edition), Pearson


Ballard, D.H. (1997). Pattern Recognition and Machine Learning. Cambridge, MA : MIT Press.

Dayan, P. and Abbot, L.F.(2001). Theoretical Neuroscience. Cambridge, MA: MIT Press.

Duda, Hart & Stork (2001). Pattern Classification. (2nd Ed.). Wiley.

Forsyth, David A. and Ponce, Jean (2003). Computer Vision: A Modern Approach. Prentice Hall.

Haykin, S. (1999). Neural Networks: A Comprehensive Foundation. Prentice Hall.

MacKay, David J.C. (2003). Information Theory, Inference, and Learning Algorithms. Cambridge University Press.

Recommended for students not specializing in computation:

O'Reilly, Randall C. and Munakata, Yuko (2000). Computational Explorations in Cognitive Neuroscience. A Bradford Book, The MIT Press.



[참고사항: 외향적인 성격 고쳐라.]