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​​1341 Probability (機率)

  • Course Time: Mon 09:10-12:00

  • Classroom: AT338

  • Course Outlines: 

    • 1. Experiments, Models and Probabilities;

    • 2. Sequential Experiments;

    • 3. Discrete Random Variables;

    • 4. Continuous Random Variables.

    • 5. Multiple Random Variables;

    • 6. Probability Models of Derived Random Variables;

    • 7. Conditional Probability Models;

    • 8. Random Vectors;

    • 9. Sums of Random Variables;

    • 10. The Sample Mean;

    • 11. Hypothesis Testing;

    • 12. Estimation of a Random Variable;

    • 13. Stochastic Processes.

  • Textbook: Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers ", the 3rd Edition, by Roy D. Yates and David J. Goodman   (John Wiley & Sons), 2015.

  • Reference Books:  

    • Introduction to Statistical Pattern Recognition, by Keinosuke Fukunaga, 2nd Edition, Academic Press, 1990.

    • Introduction to Probability and Statistics:  for Engineering and the Computing Sciences,   by J. Susan Milton, Jesse C. Arnold, Liu Kwong Ip, the McGraw Hill  Companies.

    • R in ACTION: Data analysis and graphics with R, by  Kabacoff, Robert I., Manning Publications, 2015.

  • Lecture Notes: Probability0 

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​​7766 Digital Image Processing (影像處理)

  • Course Time: Mon 18:20-21:00

  • Classroom: AT338 

  • Course Outlines: This course covers fundamental concepts and methods in digital image processing and their applications. The course is outlined as:

    • 1 Introduction

    • 2 Digital Image Fundamentals

    • 3 Intensity Transformations and Spatial Filtering

    • 4 Filtering in the Frequency Domain

    • 5 Image Restoration and Reconstruction

    • 6 Wavelet and Other Image Transforms

    • 7 Color Image Processing

    • 8 Morphological Image Processing

    • 9 Image Segmentation

    • 10 Deep Learning

    • 11 Image Classification and Object Detection with Convolutional Neural Networks.

  • Textbook: "Digital Image Processing", by R. C. Gonzalez and R. E. Woods, 4th Edition, Pearson (開發), 2017.

  • Reference Books:  "

    • Digital Image Processing Using MATLAB", 2nd Edition, by Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, 
      McGraw-Hill, 2009.

    • "Artificial Intelligence" by Leonardo Araujo dos Santos. 2018.

    •  "Hands-On Computer Vision with TensorFlow 2", by Benjamin Planche and Eliot Andres, Packt Publishing, 2019.

  • Lecture Notes: Chapter0, 關於PPT授課著作權,

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1927 深度學習概論與電腦視覺應用

  • Course Time: August 26th, 27th, and 29th  09:10-16:00

  • Classroom: S821 (Room 821@Science Building)

  • Course Outlines:

    • 1. Introduction to Machine Learning

    • 2. Multilayer Perceptron

    • 3.Deep Convolutional Neural Networks

    • 4.Object Detection and Classification

    • 5.Deep learning on the Raspberry Pi.

  • Lecture Notes: ML，MLP，DL

  • Reference Books: 

    • [1] "Artificial Inteligence" by Leonardo Araujo dos Santos. 2018.

    • [2] "Hands-On Computer Vision with TensorFlow 2", by Benjamin Planche and Eliot Andres, Packt Publishing, 2019.

    • [3] “Neural Networks and Learning Machines” by Simon O. Haykin, 3rd Edition, 2009​

  • Grade: 

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