Course Syllabus

Course Information：

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

Fall Semester
Year 2025 351865 Machine Learning 1 3.0 3 ★ Tung-Ju Hsieh 4CSIE4 23 4 資工四、資工所合開

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2025	351865	Machine Learning	1	3.0	3	★	Tung-Ju Hsieh	4CSIE4	23	4	資工四、資工所合開

Course Plan：

Instructor Tung-Ju Hsieh　Office Hours

Email tjhsieh@ntut.edu.tw

Last Updated 2026-06-08 13:49:22

Course Objective 本課程以深入淺出之方式，介紹與機器學習之相關技術及演算法，目標是修習本課程之學生具有閱讀機器學習相關文獻之基本知識，並能從事相關之實務或電腦模擬實驗之工作，因此本課程著重在廣泛介紹各類之演算法之應用，而非深究少數演算法之深奧數學原理．本課程涵蓋之演算法，包含：最佳化基本知識，隨機最佳化演算法，隨機搜尋，決策樹，隨機森林，類神經網路及深度學習相關演算法，基因演算法，貝氏定理及應用，高斯混合模型，隱藏式馬可夫鍊(及訓練演算法，支援向量機，線性及邏輯回歸，分群方法，降維方法，提升方法，決策融合技巧，及增強式學習等． This introductory course covers most of the techniques and algorithms related to the current trend in the machine learning field. The goal of this course is that students complete this course can read and understand the literature in the machine learning field, and can conduct computer simulations for experiments or apply the techniques to practical problems. Therefore, this course intends to cover a broad range of algorithms with emphasis on applications, rather than to cover only a few algorithms with extensive mathematical treatments. The algorithms covered in this course include: foundations of optiminizations, random search, decision tree, random decision forests, neural networks and deep learning, genetic algorithm, Bayes' theorem and its applications, Gauss mixture model, hidden Markov model (with its training algorithm), support vector machine, linear and logistic regressions, clustering algorithms, dimension reduction techniques (LDA, PCA, ICA, FA), boosting methods, decision

Course Schedule Week 1: Class Announcement Week 2: Introduction to Machine Learning Week 3: Basics of Supervised Learning (Classification and Regression) Week 4: VC dimension, Bayesian Decision Theory, Naive Bayes Classifiers Week 4: Dimension Reduction Techniques: PCA, FA, LDA, ICA Week 5: Unsupervised learning: Clustering Algorithm (k-means) Week 6: Decision Trees, Bagging Algorithm (Random Forest) Week 7: Basics of optimization Week 8: Basics of Optimization Week 9: Gradient Search Week 10: Feedforward Neural Networks Week 11: Multi-layer Perceptrons, Back Propagation, Regularization Methods Week 12: Deep Learning and Convolutional Neural Networks Week 13: Deep Neural Networks: YOLO, Autoencoder, LSTM Week 14: Bayesian Networks, Confidence of Classification, Self-supervised learning Week 15: SVM, Adaboost, Ensemble Learning Week 16: Final exam Week 17: Presentation Week 18: Presentation

Evaluation and grading policy Homework Projects 60% Midterm 20% Final 20 %

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:No
Course PPT files are available for students.

The access to curricular consultation Email: tjhsieh@ntut.edu.tw

Expanding teaching and resources ● 數位教材資源應用 (Application of Digital Resources)
採用影音多媒體或具開放授權之資源（如OCW、開放教科書）作為主要或輔助教材。
Adopts multimedia or open-licensed resources (e.g., OCW, Open Textbooks) as primary or supplementary teaching materials.

The course corresponds to the SDGs SDG4：優質教育（Quality Education）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

Does the course incorporate AI ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）

Note
　

Instructor	Tung-Ju Hsieh　Office Hours
Email	tjhsieh@ntut.edu.tw
Last Updated	2026-06-08 13:49:22
Course Objective	本課程以深入淺出之方式，介紹與機器學習之相關技術及演算法，目標是修習本課程之學生具有閱讀機器學習相關文獻之基本知識，並能從事相關之實務或電腦模擬實驗之工作，因此本課程著重在廣泛介紹各類之演算法之應用，而非深究少數演算法之深奧數學原理．本課程涵蓋之演算法，包含：最佳化基本知識，隨機最佳化演算法，隨機搜尋，決策樹，隨機森林，類神經網路及深度學習相關演算法，基因演算法，貝氏定理及應用，高斯混合模型，隱藏式馬可夫鍊(及訓練演算法，支援向量機，線性及邏輯回歸，分群方法，降維方法，提升方法，決策融合技巧，及增強式學習等． This introductory course covers most of the techniques and algorithms related to the current trend in the machine learning field. The goal of this course is that students complete this course can read and understand the literature in the machine learning field, and can conduct computer simulations for experiments or apply the techniques to practical problems. Therefore, this course intends to cover a broad range of algorithms with emphasis on applications, rather than to cover only a few algorithms with extensive mathematical treatments. The algorithms covered in this course include: foundations of optiminizations, random search, decision tree, random decision forests, neural networks and deep learning, genetic algorithm, Bayes' theorem and its applications, Gauss mixture model, hidden Markov model (with its training algorithm), support vector machine, linear and logistic regressions, clustering algorithms, dimension reduction techniques (LDA, PCA, ICA, FA), boosting methods, decision
Course Schedule	Week 1: Class Announcement Week 2: Introduction to Machine Learning Week 3: Basics of Supervised Learning (Classification and Regression) Week 4: VC dimension, Bayesian Decision Theory, Naive Bayes Classifiers Week 4: Dimension Reduction Techniques: PCA, FA, LDA, ICA Week 5: Unsupervised learning: Clustering Algorithm (k-means) Week 6: Decision Trees, Bagging Algorithm (Random Forest) Week 7: Basics of optimization Week 8: Basics of Optimization Week 9: Gradient Search Week 10: Feedforward Neural Networks Week 11: Multi-layer Perceptrons, Back Propagation, Regularization Methods Week 12: Deep Learning and Convolutional Neural Networks Week 13: Deep Neural Networks: YOLO, Autoencoder, LSTM Week 14: Bayesian Networks, Confidence of Classification, Self-supervised learning Week 15: SVM, Adaboost, Ensemble Learning Week 16: Final exam Week 17: Presentation Week 18: Presentation
Evaluation and grading policy	Homework Projects 60% Midterm 20% Final 20 %
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:No Course PPT files are available for students.
The access to curricular consultation	Email: tjhsieh@ntut.edu.tw
Expanding teaching and resources	● 數位教材資源應用 (Application of Digital Resources) 採用影音多媒體或具開放授權之資源（如OCW、開放教科書）作為主要或輔助教材。 Adopts multimedia or open-licensed resources (e.g., OCW, Open Textbooks) as primary or supplementary teaching materials.
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Does the course incorporate AI	● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
Note