Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2021 287173 Machine Learning 1 3.0 3 ★ SHINGCHERN YOU GCSIE
IEECSG 47 7 ◎

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2021	287173	Machine Learning	1	3.0	3	★	SHINGCHERN YOU	GCSIE IEECSG	47	7	◎

Course Plan：

Instructor SHINGCHERN YOU

Email scyou@ntut.edu.tw

Last Updated 2020-12-09 14:10:54

Course Objective This course covers a broad classes of machine learning algorithms to be able to conduct research in this field after completing this course. The emphasis of this course is not in the theoretical development (math), but in the understanding level. Therefore, many examples will be given in the lecture to show how to apply the equations to real problems. The detailed contents are given in the course schedule field.

Course Schedule Week 1: Class announcement and introduction to machine learning (CH 1) Week 2: Holiday Week 3: Basics of supervised learning (Example: k-NN), VC dimension, and regression (CH 2). Bayesian decision theory (CH 3) and Naive Bayes classifiers (CH 5) Week 4: ML and MAP estimation; Bias vs variance dilemma (CH 4). Sample mean mean and sample covariance (CH 5). Dimension reduction techniques: PCA, FA (CH 6) Week 5: Dimension reduction techniques: LDA and ICA. Clustering algorithm: k-mean (CH 7) Week 6: Unsupervised neural networks (CH 12): Competitive learning, SOFM, and ART. Decision trees: ID3, C4.5, and random forest (CH 8) Week 7: Holiday Week 8: Basics of optimization. Week 9: MT Week 10: MT sol. Gradient search and linear discrimination (CH 10) Week 11: Feedforward neural networks with examples: radical basis networks and multi-layer perceptrons. Back propagation and regularization methods (CH 11) Week 12: Deep learning and convolutional neural networks Week 13: More on deep neural networks: YOLO, Autoencoder, LSTM, and others Week 14: SVM (CH 13) Week 15: Combining multiple classifiers (CH 17) & Design and analysis of experiments (CH 19) Week 16: Reinforcement learning (Basics, Q learning, DQN) Week 17: Reinforcement learning (policy-based learning & PPO) Week 18: Final exam

Evaluation and grading policy MT 30 % Final 40 % HW 30 % Project 10 % (optional)

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:No
Reference text book: Introduction to machine learning. E. Alpaydin. 2nd ed or 3rd ed. Note: textbook is only used to follow the presentation order. Much of the detailed lecture materials are NOT covered in the textbook.

The access to curricular consultation

Note
　

Instructor	SHINGCHERN YOU
Email	scyou@ntut.edu.tw
Last Updated	2020-12-09 14:10:54
Course Objective	This course covers a broad classes of machine learning algorithms to be able to conduct research in this field after completing this course. The emphasis of this course is not in the theoretical development (math), but in the understanding level. Therefore, many examples will be given in the lecture to show how to apply the equations to real problems. The detailed contents are given in the course schedule field.
Course Schedule	Week 1: Class announcement and introduction to machine learning (CH 1) Week 2: Holiday Week 3: Basics of supervised learning (Example: k-NN), VC dimension, and regression (CH 2). Bayesian decision theory (CH 3) and Naive Bayes classifiers (CH 5) Week 4: ML and MAP estimation; Bias vs variance dilemma (CH 4). Sample mean mean and sample covariance (CH 5). Dimension reduction techniques: PCA, FA (CH 6) Week 5: Dimension reduction techniques: LDA and ICA. Clustering algorithm: k-mean (CH 7) Week 6: Unsupervised neural networks (CH 12): Competitive learning, SOFM, and ART. Decision trees: ID3, C4.5, and random forest (CH 8) Week 7: Holiday Week 8: Basics of optimization. Week 9: MT Week 10: MT sol. Gradient search and linear discrimination (CH 10) Week 11: Feedforward neural networks with examples: radical basis networks and multi-layer perceptrons. Back propagation and regularization methods (CH 11) Week 12: Deep learning and convolutional neural networks Week 13: More on deep neural networks: YOLO, Autoencoder, LSTM, and others Week 14: SVM (CH 13) Week 15: Combining multiple classifiers (CH 17) & Design and analysis of experiments (CH 19) Week 16: Reinforcement learning (Basics, Q learning, DQN) Week 17: Reinforcement learning (policy-based learning & PPO) Week 18: Final exam
Evaluation and grading policy	MT 30 % Final 40 % HW 30 % Project 10 % (optional)
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:No Reference text book: Introduction to machine learning. E. Alpaydin. 2nd ed or 3rd ed. Note: textbook is only used to follow the presentation order. Much of the detailed lecture materials are NOT covered in the textbook.
The access to curricular consultation
Note