Seminar Slides

  • Evolutionary Computation

Lecture Notes

  • Graduate Courses
    • Action-Perception-Learning Cycles (Fall 2012)
      Textbook:
      [1] Bayesian Time Series Models, Barber, Cemgil, and Chiappa, 2011.
      [2] Kalman Filtering and Neural Networks, Haykin, 2001.
      [3] Beyond the Kalman Filter, Ristic, Arulampalam, and Gordon, 2004.
    • Brain and Computation (Fall 2012)
      Textbook:
      [1] Fundamentals of Computational Neuroscience, Thomas Trappenberg, Second Edition, 2010.
      [2] Principles of Neural Science, Eric R. Kandel, J.H. Schwartz, Thomas M. Jessell, 2000.
    • Multisensory Predictive Learning (Fall 2011)
      Textbook:
      [1] Sensory Cue Integration (Eds.), Trommershaeuser, J., Koerding, K., and Landy, M. S., Oxford University Press, 2011.
      [2] Predictions in the Brain: Using Our Past to Generate a Future, (Ed.), Bar, M., Oxford University Press, 2011.
      [3] Dynamic Coordination in the Brain: From Neurons to Mind, (Eds.), Von der Malsburg, C., Phillips, W.A., and Singer, W., MIT Press, 2010.
    • Brain and Computation (Spring 2010)
      Textbook:
      [1] Fundamentals of Computational Neuroscience, Thomas Trappenberg, Second Edition, 2010.
      [2] Principles of Neural Science, Eric R. Kandel, J.H. Schwartz, Thomas M. Jessell, 2000.
    • Probabilistic Graphical Models (Artificial Neural Networks, Studies in Artificial Intelligence and Cognitive Process) (Fall 2009)
      Textbook:
      [1] Adaptive Cooperative Systems, Beckerman, M., Wiley, 1997.
      [2] Pattern Recognition and Machine Learning, Bishop, C. M., Springer, 2006.
      [3] Neural Networks: A Comprehensive Foundation, Haykin, S., Prentice Hall, 1999.
      [4] Information Theory, Inference, and Learning Algorithms, Mackay, D. J. C., Cambridge Univ.
      [5] Hypernetworks: A Molecular Evolutionary Architecture for Cognitive Learning and Memory, Zhang, B.-T., IEEE Computational Intelligence Magazine, 3(3):49-63, 2008
      Related Courses: Studies in Artificial Intelligence and Cognitive Process (Fall 2009)
    • Computational Models of Intelligence (Spring 2009)
      Textbook:
      [1] Memory: From Mind to Molecules, L. R. Squire and E. R. Kandel, 2009.
      [2] Cortex and Mind: Unifying Cognition, J. M. Fuster, Oxford University Press, 2003.
      Related Courses: Computational Neuroscience (Spring 2009)
    • Bioinformactics and Practice 1 (JAVA) (Spring 2009)
      Textbook: Java: Introduction to Problem Solving and Programming 5th edition, Walter Savitch and Frank M. Carrano , Prentice Hall, 2008.
      Related Courses: Spring 2008, Spring 2007, Spring 2006, Spring 2005, Spring 2004
    • Biological Sequence Anlaysis (Fall 2002)
      Textbook: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, Richard Durbin. S. Eddy, A. Krogh, G. Mitchison.
    • Bioinformatics and Practice I (Spring 2002)
      Textbook: Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, Second Edition, Andreas D. Baxevanis and B. F. Francis Ouellette, Wiley Inter-Science, 2001.
    • Molecular Intelligence(Knowledge Representation and Reasoning) (Spring 2002)
    • Bioinformatics(Machine Learning) (Spring 2001)
      Textbook: Bioinformatics: The Machine Learning Approach, P. Baldi and S. Brunak, A Bradford Book, The MIT Press, 1998.
    • Intelligent Learning Agents (Spring 2000)
      (Advanced Artificial Intelligence)
    • Statistical Learning Theory (Spring 1999)
      Textbook: Learning from Data: Concepts, Theory, and Methods, V. Cherkassky and F. Mulier, Wiley, 1998.
    • Machine Learning (Spring 1998)
      Textbook: Machine Learning, T. Mitchell, McGraw-Hill, 1997.
  • Undergraduate Courses
    • Artificial Intelligence: Cognitive Robotics (Fall 2012)
      Textbook:
      The Quest for Artificial Intelligence, Nilsson, N. J., Cambridge University Press, 2009.
      References:
      [1] Cognitive computing I: Multisensory perceptual intelligence in real-world (in Korean), B.-T. Zhang, Mu-Song Yeu, Communications of KIISE, 30(1):75-87, 2012.
      [2] Cognitive computing II: Machine vision-language learning in real-life (in Korean), B.-T. Zhang, Dong-Hoon Lee, Communications of KIISE, 30(1):88-100, 2012.
      [3] Cognitive computing III: Deep dynamic prediction in real-time (in Korean), B.-T. Zhang, Hyun-Soo Kim, Communications of KIISE, 30(1):101-111, 2012.
      [4] Next-generation machine learning technologies (in Korean), B.-T. Zhang, Communications of KIISE, 25(3):96-107, 2007.
      -Related Courses: Spring 2012
    • Video Search & Mining (Spring 2012)
      Textbook:
      [1] Video Search and Mining, D. Schonfeld, C. Shan, D. Tao, and L. Wang (Eds.), 2010.
      [2] Video Mining (The International Series in Video Computing), Azriel Rosenfeld, David Doermann and Daniel DeMenthon (Eds.), 2003.
    • Brain, Computation, and Neural Learning (Fall 2011)
      Textbook:
      [1] Fundamentals of Computational Neuroscience, Trappenberg, T., 2nd Ed., 2010.
      [2] Cognitive Neuroscience: The Biology of the Mind, M.S. Gazzaniga, R.B. Ivry, and G.R.Mangun, Norton & Company, 3rd Ed., 2008.
      [3] Artificial Intelligence: A New Synthesis, Nilsson, N.J., Morgan Kaufmann, 1998.
      -Related Courses: Artificial Intelligence, Brain and Computation
    • Artificial Intelligence: Biointelligence (Spring 2010)
      Textbook:
      [1]Artificial Intelligence: A New Synthesis ,Nils J. Nilsson, Morgan Kaufmann, 1998.
      [2]Cognitive Neuroscience: The Biology of the Mind, Third Edition ,M.S. Gazzaniga, R.B. Ivry, and G.R. Mangun, Norton & Company, 2008.
      -Related Courses: Fall 2009, Spring 2009, Fall 2008, Spring 2008, Spring 2007, Fall 2006, Spring 2006, Fall 2005, Spring 2005, Fall 2004
    • Biotechnology and Computing (Fall 2004)
      Textbook: New Biology: For Engineers and Computer Scientists(Pearson Education, Inc., 2004)
    • Computer Programming (JAVA) (Spring 2004)
      Textbook: Savitch, W., Java: An Introduction to Computer Science and Programming, Prentice Hall, 1999.
      Related Courses: Spring 2002, Fall 2001, Spring 2001
    • Artificial Intelligence (Fall 2004)
      Textbook: Artificial Intelligence: A New Synthesis, Nils J. Nilsson, Morgan Kaufmann, 1998.
      Related Courses: Spring 2001 
    • Data Structures (1997-2000)
      Textbook: Fundamentals of Data Structures in C++, E. Horowitz, S. Sahni, and D. Mehta, Freeman and Company, 1995.
    • Computer Practice (Unix) (1997-1999)
      Textbook: A Practical Guide to the UNIX SYSTEM(SunOS and BSD), 3rd ed., Mark G.Sobell, Addison-Wesley, 1995.
    • Introduction to Computers (1997-2002)
      Textbook: Computers: Tools for an Information Age, Sixth Edition, H. L. Capron, Addison-Wesley, 2000

Tutorial Slides


This page is maintained by Sang-Woo Lee (slee@bi.snu.ac.kr).
Last update: October 4, 2013.