Python 程式設計與數值計算

Python programming and numerical computing

學期

114-1

學分

3 學分

當期課號

555600

永久課號

CSCM30029

開課單位

國防資安管理碩士在職專班

授課教師

鄭昌杰

校區

北門

類別

選修

上課時間表

節	週二
A 18:30–19:20	Python 程式設計與數值計算 TB435 3 節連堂
B 19:30–20:20
C 20:30–21:20

* 根據陽明交大上課時間表所列

概述

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.

先修科目

This is an advanced course of Python programming. 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, PyTorch, numerical analysis, and machine learning. This course introduces how to use Python with numpy and PyTorch 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. This course includes the midterm and final reports. Each student must study and implement several research papers on machine learning published within the past three years and present a study report during the midterm and final weeks of the semester. 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PyTorch在數值分析上的程式實作,介紹如何以 Python 搭配 numpy 與PyTorch 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。本課程最後有期中與期末報告,每位學生需研讀並實作數篇近三年與機器學習相關的研究論文,並期中與期末進行報告。

教學方式

Google Colab or Anaconda

評分方式

Midterm report. (50%) Final report. (50%)

週次計畫

週次	主題
第 1 週	Introduction
第 2 週	Python programming
第 3 週	Classes
第 4 週	File I/O
第 5 週	Numpy and PyTorch - Meshes and Grids
第 6 週	Numpy and PyTorch - Indexing
第 7 週	Optimization Methods
第 8 週	Midterm exam
第 9 週	Signal Processing (interpolation and filtering)
第 10 週	Signal Processing (audio and FFT)
第 11 週	Probability-based Methods
第 12 週	Decision Tree and Random Forrest
第 13 週	Clustering
第 14 週	Generative Models
第 15 週	Paper Study
第 16 週	Paper Study

教科書

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.

Office Hours

地點: CS331
時間: By appointment
聯絡方式: E-mail: jameschengcs@nycu.edu.tw TEL: 03-5712121#31707