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
|
|
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Networking / Distance Education |
topic Discussion / Production |
Exercises |
Discussion |
Lecturing |
|
Quiz |
Written Presentation |
Oral Presentation |
Assignment |
|
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 |
Week 4 |
Implementation : Adaline SGD
Probably Approximately Correct (PAC) Learning
Agonostic PAC Learning
|
Week 5 |
Uniform Convergence
General Linear Model
Logistic Regression
|
Week 6 |
Midterm I |
Week 7 |
Implementation: A General Flow of Learning Process
No Free Lunch Theorem
Bias-Variance TradeOff
|
Week 8 |
Convex Learning Problems
Surrogate Function
|
Week 9 |
Stability of Strongly Convex Learning Problems
Support Vector Machine
Kernel Trick
|
Week 10 |
The Representer Theorem
KNN
Decision Tree
Implementation: Kernel SVM, KNN, Decision Tree, and Random Forest
|
Week 11 |
Data Preprocessing
L1 Regularization
Implementation: Feature Selection
Principle Component Analysis
|
Week 12 |
Midterm II
|
Week 13 |
LDA
Kernel PCA
Implementation: Kernel PCA
Machine Learning Pipeline and Cross Validation
|
Week 14 |
Learning Curve
Imbalanced Data
Majority Voting
Implementation: Majority Voting and Parameter Tuning
Bagging
AdaBoost |
Week 15 |
Clustering and K-means
Determine the number of clusters
Agglomerative Clustering
DBSCAN
|
Week 16 |
Regression
Final Exam
Final Report |
self-directed learning |
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Evaluation |
(1) 期中考 I (20%)
(2) 期中考 II (20%)
(3) 期末考 (20%)
(4) 期末報告 (40%) |
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 |
To Be Determined. |
Sustainable Development Goals, SDGs(Link URL) |
| include experience courses:N |
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