NCHU Course Outline

Course Name	(中) 機器學習(7759)
	(Eng.) Machine Learning
Offering Dept	Executive Master Program in Big Data
Course Type	Elective			Credits	3	Teacher	Kuan-Chu Peng
Department	Executive Master Program in Big Data / (W)Graduate			Language	Chinese	英文/EMI		Semester	2025-SPRING
Course Description	In this course, we will focus on the algorithmic perspective of machine learning. Specifically, the implementation of the classical and the modern algorithms with python and TensorFlow. The major objective of the course is to establish students’ ability to read and to implement the modern machine learning algorithms in the recent research papers. And prepare the students to their future research works.
Prerequisites								self-directed learning in the course	Y

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			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 Final Report/Final Exam(Take-Home) Week 17 自主跨域學習 Week 18 自主跨域學習
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https://sites.google.com/email.nchu.edu.tw/mlcourse/structural-machine-learning-models-and-its-applications )
Office Hours
To Be Determined.
Sustainable Development Goals, SDGs(Link URL)
include experience courses：N

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