用Python於數據科學和機器學習
Lab on Python for data science and machine learning
| 節 | 週三 |
|---|---|
5 13:20–14:10 | 用Python於數據科學和機器學習 ED303 3 節連堂 |
6 14:20–15:10 | |
7 15:30–16:20 |
* 根據陽明交大上課時間表所列
This course covers both theoretical and practical aspects of applied data science, analytics, and visualization in Python. We will start from general python programming basics, data structures, and algorithm design with a heavy emphasis on applying data analysis and visualization techniques to solve real-world problems in different domains. Topics include data representation, manipulation and clearing, visualization, regression, convolutional and recurrent neural networks, reinforcement learning, model development and evaluation with most up-to-date Python modules and popular toolkits.
Basic programming skills
Grading policy will be explained in the first lecture and posted as a video on YouTube
| 週次 | 主題 |
|---|---|
| 第 1 週 | Course Introduction, overview of Python, basic elements of Python, and first Python program |
| 第 2 週 | Fundamental programming concepts I including Syntax and semantics, variables, expressions, assignments, selections, and loops. Pandas and Numpy libraries. |
| 第 3 週 | - Introduction to Regression kNN and Linear Regression - Multi-linear and Polynomial Regression |
| 第 4 週 | - Model Selection and Cross Validation - Regularization Ridge and Lasso Regression |
| 第 5 週 | - Probability - Inference in Regression and Hypothesis Testing |
| 第 6 週 | - Missing Data & Imputation - Principal Component Analysis |
| 第 7 週 | Midterm presentation |
| 第 8 週 | - Visualization - Ethics |
| 第 9 週 | - Logistic Regression 1 - Logistic Regression 2 |
| 第 10 週 | - Decision Tree - Bagging |
| 第 11 週 | - Random Forest - Boosting |
| 第 12 週 | - Model Interpretability - Experimental Design |
| 第 13 週 | - NLP 1 - NLP 2 |
| 第 14 週 | - Project Submission Deadline |
| 第 15 週 | Final Project presentation I |
| 第 16 週 | Final Project presentation II |
PRIMARY REFERENCE • Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud by Paul J. Deitel , and Harvey Deitel OTHERS: • Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming by Eric Matthes ISBN-10: 1593279280 ISBN-13: 978-1593279288 ISBN-13: 978-0135404676 ISBN-10: 0135404673 • Practice of Computing Using Python, The, Student Value Edition,3rd Edition, by William F. Punch, and Richard Enbody ISBN-13: 978-0134380315 ISBN-10: 0134380312 • Python for Everyone, 2nd Edition by Cay S. Horstmann, Rance D. Necaise ISBN-13: 978-1119056553 ISBN-10: 1119056551 • Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Editionby Wes McKinney ISBN-13: 978-1491957660 ISBN-10: 1491957662 • Python for Software Design: How to Think Like a Computer Scientist 1st Edition by Allen B. Downey (Author). Available at http://www.greenteapress.com/thinkpython/thinkpython.html ISBN-13: 978-0521725965 ISBN-10: 0521725968 • Automate the Boring Stuff with Python: Practical programming for total beginners by Al Sweigart. Available at https://automatetheboringstuff.com/ ISBN-10: 1593275994 ISBN-13: 978-1593275990
- 地點
- ED716A
- 時間
- Anytime
- 聯絡方式
- stefano@nycu.edu.tw