Python 科學計算程式設計

This is an advanced course of Python programing. Please DO NOT take this course if you never study any course of basic programming with Python. The main topics of this course are numpy, numerical analysis, and machine learning. This course will not introduce the basic Python syntax and instructions.

This is an advanced course of Python programing. Please DO NOT take this course if you never study any course of basic programming with Python. The main topics of this course are numpy, numerical analysis, and machine learning. This course will not introduce the basic Python syntax and instructions. This course introduces how to use Python with numpy to design a program that efficiently calculates a large number of floating point values. The course topics cover document processing, scientific data analysis, signal processing, mathematical model program implementation, and basic image processing. Programming assignment every week, with a total of about 12 assignments. The mid-term exam is a paper-based exam with 10 completion questions and 2 programming questions. The final project at the end of this course, which students complete in teams of 1-3 members per team. Use the machine learning model introduced in class to apply it to self-collected data sets to complete the prediction of certain events. 此為Python程式設計的進階課程,若您沒有修過Python基礎程式設計之相關課程,請勿選修本課程。本課程主要內容為numpy與數值分析,並沒有包含Python基礎語法與指令。 此課程介紹如何以 Python 搭配numpy 來設計一個有效率地運算大量浮點數值的程式, 課程主題涵蓋文件處理、科學資料分析、訊號處理、機器學習模型程式實作與基本的影像處理。本課程每週都會有程式設計的作業,總共會有約12次的作業。本課程有期中上機測驗,在三小時完成10題填充題與兩題程式設計題。本課程最後有期末專案,學生組隊完成,每隊1-3人。利用課堂所介紹的機器學習模型套用在自行收集的資料集,完成某種事件的預測。 * This is a hybrid course with virtual and physical classrooms. * Virtual classroom:https://meet.google.com/rqi-pcty-jie * Physical classroom: ITSC, PC3, Guangfu Campus, Hisnchu. * Students may join this course if the number of attendees is below 50 before the registration deadline. To enroll, please email the instructor during the second week of the semester with your student ID, department, grade level, and course type. Priority is given to non-EE and CS students.(若在選課截止日前三天上課人數仍未滿50人,即可加簽。非電機與資訊學院的同學優先。欲加簽的同學,請於開學第二周,將您的學號、系級、姓名、課程分類email給老師即可。不在這時間內寄信者,恕不受理)。

Google Colab or Anaconda

Homework. (40%) Midterm exam. (30%) Final project. (30%)

1. Claus Führer, Jan Erik Solem, and Olivier Verdier, "Scientific Computing with Python: High-performance scientific computing with NumPy, SciPy, and pandas, 2nd Edition," Packt Publishing, July 23, 2021. 2. John D. Kelleher, Brian Mac Namee and Aoife D'Arcy, "Fundamentals of Machine Learning for Predictive Data Analytics," 2nd, MIT Press, 2020. 3. Aurélien Géron, "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems," 3rd, O'Reilly Media, 2022.

地點

CS331

時間

By appointment

聯絡方式

E-mail: jameschengcs@nycu.edu.tw TEL: 03-5712121#31707