機器學習概論
Introduction to Machine Learning
| 節 | 週二 |
|---|---|
5 13:20–14:10 | 機器學習概論 EC016（光復） 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 is a course aimed to provide an foundation of advanced grad-level courses, e.g. machine learning and data mining.
Computer Programming, Data Structures, Introduction to Algorithms
無備註
Onsite lectures + lecture notes
Only tentative: a. Quiz (20%); b. Mid-term (30%) and Final (30%); c. Term Project(s) (20%)
教師未提供此項資料
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction: 1. Why machine learning 2. Concept learning 3. Inductive Learning |
| 第 2 週 | Data Preparation: 1. Data types 2. Data quality 3. Data preprocessing |
| 第 3 週 | Modeling: 1. Parametric models 2. Non-parametric models |
| 第 4 週 | Decision tree learning: 1. Tree construction 2. Tree pruning 3. From trees to rules |
| 第 5 週 | Bayesian learning 1. Bayesian decision 2. Naive Bayes |
| 第 6 週 | Neural Nets: 1. Perceptron 2. Multilayer perceptrons 3. Backprop |
| 第 7 週 | Clustering: 1. Partition-based 2. Density-based clustering |
| 第 8 週 | Evaluation: 1. Methodology 2. Performance measures |
| 第 9 週 | Other issues: 1. Algorithm-centric vs. Data-centric 2. Predictive performance vs. comprehensibility |
1. Machine Learning by Tom M. Mitchell, 1997. 2. Machine Learning for predictive data analytics by Kelleher, Namee, D’Arcy, 2015. 3. Introduction to Machine Learning by Alpaydin, 2020.
- 地點
- EC332C
- 時間
- Mon 9~10AM
- 聯絡方式
- By appointment