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 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
The course follows a challenge-based learning framework (see below) and is fully online. -> We will use slack to communicate to the class. add yourself here https://join.slack.com/t/labonpythonfo-5n45232/shared_invite/zt-3c0ih6vsm-ffSG5acu2lQLD5m2vycYAw make sure you turn on the notifications -> The calendar event is here: add it to your schedule https://calendar.app.google/JwVPrQbmYWNa1PCz9 -> The class will follow the schedule of this class https://harvard-iacs.github.io/2021-CS109A/pages/schedule.html you can review the slides ahead of class
Basic programming skills
The class is based on challenge-based learning -> A set of slides describing the class is here https://docs.google.com/presentation/d/10r_Re0EfMxNw2qzSBKBooFijly-mD0ZyfSxtWAKz1OY/edit?usp=sharing -> a detailed document describing the challenge based learning framework is here https://docs.google.com/document/d/1SPG1QQbXQU8UV9CYrUpvyfAkQdj1rtvzN9zwm6OfRe8/edit?usp=sharing -> A video of the instructor describing the framework is here https://youtu.be/8_D2Hoaqp2o -> Some class recordings are here https://drive.google.com/drive/folders/16vuLkEf7u8iHqvt7IcTrd_Ld0NhXLQ6Q?usp=drive_link
The grading policy is explained in this document https://docs.google.com/document/d/1SPG1QQbXQU8UV9CYrUpvyfAkQdj1rtvzN9zwm6OfRe8/edit?usp=sharing
| 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 Syntaxand 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 & amp
 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
- online
- 時間
- just message me on slack
- 聯絡方式
- rini.stefano@gmail.com