In this lecture, students will learn about mathematical theory and other topics related to machine learning.
By the end of this course, students will be able to:
1. Understand the theoretical properties of machine learning and can apply them to real problems.
Optimization, Machine learning
✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Attendance is taken in every class.
Students are required to read the text before coming to class.
Course schedule | Required learning | |
---|---|---|
Class 1 | nonlinear optimization 1 | We instruct in each class |
Class 2 | nonlinear optimization 2 | We instruct in each class |
Class 3 | Supervised learning 1 | We instruct in each class |
Class 4 | Supervised learning 2 | We instruct in each class |
Class 5 | SVM 1 | We instruct in each class |
Class 6 | SVM 2 | We instruct in each class |
Class 7 | mid-term test | We instruct in each class |
Class 8 | clustering 1 | We instruct in each class |
Class 9 | clustering 2 | We instruct in each class |
Class 10 | feature extraction 1 | We instruct in each class |
Class 11 | feature extraction 2 | We instruct in each class |
Class 12 | generative model 1 | We instruct in each class |
Class 13 | generative model 2 | We instruct in each class |
Class 14 | mid-term test | We instruct in each class |
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
None required
Course materials can be found on T2SCHOLA
Students will be assessed on their understanding of machine learning and text mining.
Students' course scores are based on tests and reports.
No prerequisites