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Course title |
Python Programming for Intelligent Medicine |
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Semester |
111-1 |
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Designated for |
VARIOUS PROGRAM Intelligent Medicine Program |
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Instructor |
SHANG-TSE CHEN |
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Curriculum Number |
IMP5004 |
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Curriculum Identity Number |
P56 U9030 |
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Class |
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Credits |
3.0 |
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Full/Half
Yr. |
Half |
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Required/
Elective |
Required |
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Time |
Thursday 2,3,4(9:10~12:10) |
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Remarks |
The upper limit of the number of students: 42. |
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Course introduction video |
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Table of Core Capabilities and Curriculum Planning |
Association has not been established |
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Course Syllabus
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Please respect the intellectual property rights of others and do not copy any of the course information without permission
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Course Description |
Course Web: https://cool.ntu.edu.tw/courses/21265
*This course will be taught in English*
Artificial Intelligence has been taking an increasingly important role in medical applications. This course is for medical students to learn basic Python programming, data processing and analysis, machine learning algorithms and their applications on medical problems such as medical image analysis. |
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Course Objective |
Artificial Intelligence has been taking an increasingly important role in medical applications. This course is for medical students to learn basic Python programming, data processing and analysis, machine learning algorithms and their applications on medical problems such as medical image analysis. |
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Course Requirement |
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Student Workload (expected study time outside of class per week) |
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Office Hours |
Note: Instructor: Professor Shang-Tse Chen, email: stchen@csie.ntu.edu.tw
Office hour: after class or by appointment
TA: Bo-Han Kung, email: d10922019@csie.ntu.edu.tw
TA office hour: 11:00-12:00 Monday @ CSIE building, room 405 (or by appointment) |
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Designated reading |
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References |
1. A. B. Downey, Think Python 2nd ed., O'Reilly Media, 2015. ISBN:
9781491939369
https://greenteapress.com/wp/think-python-2e
2. W. McKinney, Python for Data Analysis, 2nd ed., O'Reilly Media, 2012.
ISBN: 9781449319793
https://github.com/wesm/pydata-book |
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Grading |
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No. |
Item |
% |
Explanations for the conditions |
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1. |
Homeworks |
40% |
10% x4 |
2. |
Midterm |
30% |
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3. |
Final |
30% |
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Adjustment methods for students |
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Teaching methods |
Assisted by video |
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Assignment submission methods |
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Exam methods |
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Others |
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Week |
Date |
Topic |
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第1週 |
9/08 |
* Course introduction
* Python environment setup
* Variables, expressions and statements |
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第2週 |
9/15 |
* Functions |
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第3週 |
9/22 |
* Conditionals Control Flow |
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第4週 |
9/29 |
* Iteration & For-Loops |
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第5週 |
10/06 |
* Strings |
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第6週 |
10/13 |
* Recursion |
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第7週 |
10/20 |
* Set and Dict |
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第8週 |
10/27 |
Midterm Exam |
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第9週 |
11/03 |
* Classes and Objects |
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第10週 |
11/10 |
* Numpy |
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第11週 |
11/17 |
* Data Analysis with Numpy and Pandas |
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第12週 |
11/24 |
* ML Libraries in Python |
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第13週 |
12/01 |
* Data Preprocessing |
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第14週 |
12/08 |
* Data Clustering |
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第15週 |
12/15 |
* Dimension Reduction |
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第16週 |
12/22 |
Final Exam |