MAEG5735: Applied Computational Intelligence

Instructor: Charlie C.L. Wang
Description Coursenotes Assessment Download Copyright News
NEWS
  • The documents of Assignment 1 (Due: 23:59 Feb 9, 2020) can be downloaded from here. For submission, please send your assignment to TA directly by email .
  • The course homepage is opened. (January 6, 2020)
    • COURSE DESCRIPTION
    Computational intelligence is an integral part of many commercial applications and research projects today, in areas ranging from medical diagnosis and treatment to finding your friends on social networks. Many people think that machine learning can only be applied by large companies with extensive research teams. In this course, we will show you how easy it can be to build machine learning solutions yourself, and how to best go about it. With the knowledge learned in this course, you can build your own system for finding out how people feel on Twitter, or making predictions about global warming. We made a conscious effort not to focus too much on the math, but rather on the practical aspects of using machine learning algorithms.

    Teaching Assistants:
    Zishun Liu (Email: liuzishun@gmail.com)

    Reference Books:
    [1] Muller, A.C. and Guido, S. Introduction to Machine Learning with Python, O'Reilly Media Inc., 2017.
    [2] Duda, R., Hart, P.E. and Stork, D.G. Pattern Classification, Wiley-Interscience, 2001.
    [3] Goodfellow, I., Bengio, Y. and Courville, A. Deep Learning, The MIT Press, 2017.
    [4] Chollet, F. Deep Learning with Python, Manning Publications Co., 2018.
    COURSENOTES
    L1 - Introduction [Python_Code]
    L2 - Supervised Learning I [Python_Code]
    L3 - Supervised Learning II [Python_Code]
    L4 - Unsupervised Learning: Transformation [Python_Code]
    L5 - Unsupervised Learning: Clustering [Python_Code]
    L6 - Representing Data and Engineering Features [Python_Code]
    L7 - Model Evaluation and Improvement [Python_Code]
    L8 - Working with Text Data [Python_Code]
    ASSESSMENT
    100% - Course Project (formed by four implementation assignments)
    DOWNLOAD
    Useful libraries can be used in completing the course assignments and projects are listed below.
    COPYRIGHT
    All rights about the content listed on this page are reserved by Charlie C.L. Wang at the Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong. In no event shall the author be liable to any party for direct, indirect, special, incidental, or consequential damage arising out of the use of the materials downloaded from this page.
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