Notice for EIE2108 (+EIE2106) FIMT students



LAST UPDATED TIME: Nov 14, 10:00:00 HKT 2022



What's new:

 

Notes

Textbook & References

Tutorial

Quiz result

Lab material

Useful info

Supplementary materials

Links

Self-learning

Q and A

Please visit this homepage frequently to update your information.

·        Weighting of different components:

o   Exam: 0%

o   Continuous Assessment.: 100%:

§  lab activities: 24%

§  projects: 16%

§  test: 40%

§  quizzes and assignments: 20%


 

·        The final test on Nov 21 will be held in room HJ303 (Not in N003!) (2022/11/14) 

·        Solution of Tutorial question sets 4 & 5 are uploaded.  (2022/11/7) 

·        A mock test paper is here for your reference. This paper was set for an online test in which students could use computers to do some computation. Hence the paper contains some elements that rely on a computer to handle. We will have an on-site test this year and you can expect that these elements will be removed in the coming test.    https://www.eie.polyu.edu.hk/~enyhchan/a_FIMT21_files/image003.gif  (2021/11/1)

·        Solution of tutorial question set 3 is uploaded.  (2022/10/27)

·        Tutorial question sets 3 & 4 are uploaded.  (2022/10/22) 

·        A typo mistake is found in Table 2 of the Lab sheet. It’s corrected. We updated the FAQ page and the data set for task 3 accordingly. See the FAQ page for the details.   (2022/10/12)

·        Solution of Tutorial question set 2 is uploaded.  (2022/9/27)

·        Details of our lab activities are uploaded. Please refer to the Lab material section.  (2022/9/19)

·        Tutorial question set 2 is uploaded.  (2022/9/19)

·        Solution of Tutorial question set 1 is uploaded.  (2022/9/19)

·        A 5-minute online quiz will be held on Sep 26.  (2022/9/19)


 

Tentative schedule is as follows:

Tentative schedule   



Notes

!! My ppt version is subject to change as I may update it for better presentation.
!! You may download the newest version of handouts here.

  • Python programming                               [1in1 version]
  • Python programming - tasks                   [1in1 version]
  • Matrix theory                                              [1in1 version]

o    A python code example for computing eigenvalues is here.

  • Calculus                                                         [1in1 version]
  • Complex number                                       [1in1 version]
  • Digital rights management                      [1in1 version]
  • Artificial intelligence                                 [1in1 version]


Textbook and references:
 

·         Python Tutorial available at  https://www.w3schools.com/python/default.asp

·         S. Nagar, Introduction to Python for Engineers and Scientists, Cambridge University Press 2014.: Open Source Solutions for Numerical Computation, Apress, 2018 (A link  for downloading the e-version is here)

·         F. Faisal, S.J. Russell and P. Norvig, Artificial Intelligence A Modern Approach (4th Edition), Pearson, 2021 (A link  for downloading the e-version of the 3rd ver is here.)



Tutorial arrangement

·        There is no tutorial session in week 1. 

Schedule: 

Week

Venue

Time

2-13

N003

Mon, 5:30pm-6:20pm

 



Tutorial problems:

 (Note: Tutorial 1 means it is the 1st tutorial question set in a sequence of question sets. It does not imply that it has to be discussed in tutorial session 1.) 



Revision guidelines:

  • Do revision based on the tutorial question sets, quizzes, exercises and lab tasks.


Quiz and test results:

There will be quizzes or classworks in weeks 3 to 13.

·        The exact time and date of a quiz will be confirmed 1 week in advance.

·        Total ceiling is 20% 

 You can always seek for help from me.



Laboratory materials:

  • Lab schedule: 

Date

Wed, weeks 8, 9, 10 & 11

Time

8:30 am - 11:20 am

Venue

CF502 + CF503 + CF504

 

  • Lab activities:
    • Details of the lab assignments (including the project) will be available here.   (updated 2022/10/12)
    • 5 datasets for task 2 are here. Use data set No. n in your test, where n = 1 + ((sum of all digits of your student numbers) mod 5). For example, if your student number is 19435207, then use data set 2 (because 1 + ((1+9+4+3+5+2+0+7) mod 5) = 2).
    • A dataset for you to verify your code for realizing task 3 will be available here.  (updated 2022/10/12)

·         FAQ for the lab activities/project of 2022 will be here.   (updated 2022/10/12)



Some useful information:

 

·        Cheat sheets for python programming are available in https://hakin9.org/wp-content/uploads/2020/02/beginnersPythonCheatSheet.pdf   or  https://www.pythoncheatsheet.org/

·        Cheat sheets for mathematics  are available in  https://doubleroot.in/cheat-sheets/

Before you use them, make sure that you know how to use them. Somebody else's medicine could be poison to you. 



Useful links:

·         A good website for you to learn Python from ground 0:   https://www.w3schools.com/python/

·         A place where you can get an easy-to-start Python distribution:     https://www.anaconda.com/products/individual  (Look for Anaconda Installers)

Its user guide is available at https://docs.anaconda.com/anaconda/user-guide/

·         Door to Matplotlib: https://matplotlib.org/3.2.1/contents.html

·         Door to Numpy: https://numpy.org/

Some clips available in Youtube

·         How to install Anaconda Python, Jupyter Notebook, and Spyder on Windows 10  :  https://www.youtube.com/watch?v=5mDYijMfSzs  (It shows you how to download and install Anaconda and provides you some information about Jupitor notebook + Spyder.)

·         Introduction to the Spyder IDE for Python : https://www.youtube.com/watch?v=zYNRqVimU3Q

·         Complete Python NumPy Tutorial (Creating Arrays, Indexing, Math, Statistics, Reshaping) https://www.youtube.com/watch?v=GB9ByFAIAH4

    • @1:35: It explains why processing Numpy array is much faster than processing lists.
    • It’s well organized with a detailed video timeline.

 

Software:

·         Download anaconda from https://www.anaconda.com/products/individual  (Look for Anaconda Installers) and install it in your computer. We will use it to do tutorials and lab activities in class. Get it ready before week 3.

·         Anaconda is bundled with some commonly used packages such as Numpy and MatPlotlib. You don’t need to separately install them. After installing Anaconda, you only need to import them to your program when using them.

·         Anaconda is bundled with some development tools such as Spyder. Spyder is a useful integrated development environment (IDE) for you to develop Python code. We will use it in class.

·         You need them to do assignments and laboratory activities, so better get familiar with them as soon as possible.