We will learn ...
Machine learning has been shown powerful on many specific problems. While there are many existing packages to perform various machine learning tasks, we must understand the relations between the parameters and the outcome. This course will start with an overview of linear algebra, which is the foundation of many machine learning algorithms. Then you will learn the theory behind each algorithm and how to implement them from scratch (which should be fun!). With these insights, you will be more confident in picking the models, tuning the hyperparameters, and even contriving new algorithms.
You need to do ...
HW0: Tell me your email before February 27 to get extra 2pt — this is a required work. Important information will be announced through email.
Linear Algebra with NumPy
LA exam (20%):
Machine Learning with NumPy
ML exam (20%):
Homework (60%):
There will be a group assignment in class every week. You must submit your answer before the class ends. We will invite some volunteers to present their answers, and extra points will be given to those who present.
A few tips for learning mathematics ...
Mistakes Make You Smarter: Everyone learns through experiences and mistakes. For each new concept you learn, generate as many examples as possible to train your brain to distinguish between right and wrong.
Ask Questions: Beyond knowledge, mathematics is fundamentally about logic. Question everything you encounter—why it is defined this way, why an assumption is required, why a proof needs a particular step, and so on.
Think Carefully: Sound arguments should hold true in any circumstance. Verify the examples you generate to ensure they align with your argument.
Help Each Other: Learning together can make the process easier. Teaching others is also an effective way to reinforce your own understanding.
Course Info
- Term: Feb 23, 2026 – June 12, 2026
- Meeting time: Monday, 9:00 am – 12:00 noon @ SC201
- Instructor: Jephian Lin | 林晉宏
- Email: jephianlin [at] nycu.edu.tw
- Office: SA336
- Office Hours: By appointment
- TA: Muen Chen | 陳慕恩
- Email: a0987572109 [at] gmail.com
- Office Hours: By appointment
- TA: Bolin Lai | 賴柏霖
- Email: mcc61015 [at] gmail.com
- Office Hours: By appointment
Textbook
Linear Algebra with NumPy
Jephian Lin
Machine Learning with NumPy
Jephian Lin
Resources for Python
A Whirlwind Tour of Python [on GitHub]
Jake VanderPlas, O'Reilly Media, 2016
Python Data Science Handbook [on GitHub]
Jake VanderPlas, O'Reilly Media, 2016Course website
Neural Networks and Deep Learning
Michael Nielsen
CommonMark (You may find Markdown tutorials here.)
Resources for Linear Algebra
Essence of linear algebra
3Blue1Brown
Linear Algebra
Jim HefferonCourse website
Tentative Schedule
Policies/Ethics
Accessibility
Students with diverse learning styles and needs are welcome in this course. In particular, if you have a disability/health consideration that may require accommodations, please feel free to approach me.
Grading
Percentage scores will be converted to letter grades according to Regulations for Grading of Students (國立陽明交通大學學生成績作業要點).
Attendance
You are expected to attend the classes.
Missing work
If you miss some course components due to illness, accident, family affliction, or religious observances, please talk to me and provide the documentation. In such cases, the course component is excused, and your course score will be calculated by distributing the weight of the missed item(s) across the other course components. Missing components are limited to at most 20%.
Academic integrity
Do not copy others' work, including others' homework, the textbook, online materials, and others' answers in an exam; if it is really necessary, add proper citations to your references. It makes no point (and gives you no point) if the work is not yours since you learned nothing.