| 週次 |
授課內容 |
| 第1週 |
Course Logits
What is Machine Learning?
Type of ML
Implementation: Data Plotting |
| 第2週 |
Formal Model
Perceptron |
| 第3週 |
Implementation: Perceptron
AdaLine and Gradient Descent
Implementation : Adaline |
| 第4週 |
Implementation : Adaline SGD
Probably Approximately Correct (PAC) Learning
Agonostic PAC Learning
|
| 第5週 |
Uniform Convergence
General Linear Model
Logistic Regression
|
| 第6週 |
Midterm I |
| 第7週 |
Implementation: A General Flow of Learning Process
No Free Lunch Theorem
Bias-Variance TradeOff
|
| 第8週 |
Convex Learning Problems
Surrogate Function
|
| 第9週 |
Stability of Strongly Convex Learning Problems
Support Vector Machine
Kernel Trick
|
| 第10週 |
The Representer Theorem
KNN
Decision Tree
Implementation: Kernel SVM, KNN, Decision Tree, and Random Forest
|
| 第11週 |
Data Preprocessing
L1 Regularization
Implementation: Feature Selection
Principle Component Analysis
|
| 第12週 |
Midterm II
|
| 第13週 |
LDA
Kernel PCA
Implementation: Kernel PCA
Machine Learning Pipeline and Cross Validation
|
| 第14週 |
Learning Curve
Imbalanced Data
Majority Voting
Implementation: Majority Voting and Parameter Tuning
Bagging
AdaBoost |
| 第15週 |
Clustering and K-means
Determine the number of clusters
Agglomerative Clustering
DBSCAN
|
| 第16週 |
Regression
Final Exam
Final Report |
自主學習
內容 |
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