Type of Credit: Elective
Credit(s)
Number of Students
This course provides a practical approach to machine learning, where students will explore how to implement machine learning algorithms on real-world datasets. The course will cover the most commonly used machine learning algorithms and how to use the Scikit-Learn library to develop complete machine learning projects. The course will also explain the intersection of machine learning and business analytics, providing students with the necessary tools to apply machine learning models effectively in real-world scenarios. In addition, students will learn how to model, analyze, and validate data using machine learning concepts to extract valuable insights from diverse data sources. The course includes hands-on experimentation with various datasets, where students will learn about both supervised and unsupervised machine learning techniques, such as classification, regression, clustering, feature selection, and dimensionality reduction.
能力項目說明
By the end of this course, students will have gained the following skills:
1. Analyzing and selecting appropriate machine learning algorithms based on specific problem domains and data characteristics.
2. Collecting, preprocessing, and effectively cleaning data, preparing it for machine learning tasks.
3. Building and fine-tuning machine learning models to achieve optimal performance in various applications.
4. Evaluating the performance of machine learning models using metrics, cross-validation, and hyperparameter tuning.
5. Interpreting the results of machine learning models and using them to inform decision-making processes in business analytics.
6. Demonstrating a solid understanding of supervised and unsupervised learning techniques.
7. Considering ethical considerations, potential biases, and the responsible use of machine learning in practical scenarios.| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
|
週次 |
課程主題 |
課程內容與指定閱讀 |
教學活動與作業 |
|
1 |
Introduction |
Introduction for Machine Learning |
|
|
2 |
Supervised Learning |
Supervised Learning ＆ Linear Models for Regression |
|
|
3 |
Supervised Learning |
Supervised Learning ＆ Linear Models for Classification |
Quiz 1 |
|
4 |
Supervised Learning |
Model Evaluations ＆ Metrics |
Quiz 2 |
|
5 |
Supervised Learning |
Supervised Learning ＆ SVM |
Quiz 3 |
|
6 |
Supervised Learning |
Trees, Forests ＆ Ensemble Learning |
Quiz 4 |
|
7 |
Review |
Midterm Exam Review |
Quiz 5 |
|
8 |
Exam |
Midterm Exam |
|
|
9 |
Unsupervised Learning |
Unsupervised Learning ＆Density Estimation |
|
|
10 |
Unsupervised Learning |
Unsupervised Learning ＆ Clustering |
Quiz 6 |
|
11 |
Unsupervised Learning |
Unsupervised Learning ＆ Dimensionality Reduction |
Quiz 7 |
|
12 |
ML Topic |
Feature Selection & Model Parameter Tuning |
Quiz 8 |
|
13 |
ML Topic |
Working with Imbalanced Data |
Quiz 9 |
|
14 |
Project Presentation |
Final Project Presentation |
Quiz 10 |
|
15 |
Review |
Final Exam Review |
|
|
16 |
Exam |
Final Exam |
|
10 Quizzes 40%
Midterm Exam 20%
Final Exam 20%
Final Project 20%Required
The documentation for the latest version of Scikit-learn
https://scikit-learn.org/stable/user_guide.html
Recommended
Géron, A., (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition. O'Reilly Media, Inc. ISBN:9781492032649https://scikit-learn.org/stable/user_guide.html