| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
(1) 了解機器學習演算法的理論基礎
(2) 能夠實做機器學習演算法
(3) 能夠將機器學習的演算法應用到實際的問題
(4) 能夠清晰地說明並報告機器學習於實際應用時的方法原理、遇到的困難與展示最後的成果 |
| 3.Professional Knowledge in Statistical Analysis |
| 5.Professional Knowledge in Computer Science |
| 7.Mathematical and Statistical software skills |
|
|
| topic Discussion / Production |
| Networking / Distance Education |
| Discussion |
| Lecturing |
|
| Written Presentation |
| Oral Presentation |
| Assignment |
| Quiz |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
Course Logits
What is Machine Learning?
Type of ML
Implementation: Data Plotting |
| Week 2 |
Formal Model
Perceptron |
| Week 3 |
Implementation: Perceptron
AdaLine and Gradient Descent
Implementation : Adaline
Implementation : Adaline SGD
Probably Approximately Correct (PAC) Learning
Agonostic PAC Learning |
| Week 4 |
Uniform Convergence
General Linear Model
Logistic Regression |
| Week 5 |
Implementation: A General Flow of Learning Process
No Free Lunch Theorem
Bias-Variance TradeOff |
| Week 6 |
Midterm I |
| Week 7 |
Convex Learning Problems
Surrogate Function |
| Week 8 |
Stability of Strongly Convex Learning Problems
Support Vector Machine
Kernel Trick |
| Week 9 |
The Representer Theorem
KNN
Decision Tree
Implementation: Kernel SVM, KNN, Decision Tree, and Random Forest |
| Week 10 |
Data Preprocessing
L1 Regularization
Implementation: Feature Selection
Principle Component Analysis |
| Week 11 |
LDA
Kernel PCA
Implementation: Kernel PCA
Machine Learning Pipeline and Cross Validation |
| Week 12 |
Midterm II |
| Week 13 |
Learning Curve
Imbalanced Data
Majority Voting
Implementation: Majority Voting and Parameter Tuning
Bagging
AdaBoost |
| Week 14 |
Clustering and K-means
Determine the number of clusters
|
| Week 15 |
Agglomerative Clustering
DBSCAN |
| Week 16 |
Final Report, Hand out the take-home final Exam |
| Week 17 |
自主跨域學習 |
| Week 18 |
自主跨域學習 |
|
| Evaluation |
(1) 期中考 I (30%)
(2) 期中考 II (30%)
(3) Take-Home 期末作業 (15%)
(4) 期末報告 (25%) |
| Textbook & other References |
1. Python Machine Learning, Sebastian Raschka
2. Introduction to Machine Learning, Ethem Alpaydm. |
| Teaching Aids & Teacher's Website |
自編投影片教材、課堂筆記及課程錄影
(請於開學後參考本課程網頁 https://sites.google.com/email.nchu.edu.tw/mlcourse/machine-learning?authuser=0) |
| Office Hours |
| Plase email to gjpeng@mail.nchu.edu.tw to make the appointment. |
| Sustainable Development Goals, SDGs(Link URL) |
| 04.Quality Education 08.Decent Work and Economic Growth 09.Industry, Innovation and Infrastructure | include experience courses：N |
|