Introduction

Welcome to the Advanced Machine Learning course! This course is designed to provide an in-depth understanding of advanced topics in machine learning, including data comprehension, advanced machine learning methods, optimization approaches, what to watch out when working with each machine learning model, and more. By the end of this course, you will have a solid grasp of various types of datasets, machine learning techniques and be able to apply them to real-world problems.

Course Criteria

`Criteria`	`Percentage`
Attendance	10%
Participation & quiz	10%
Midterm Exam or/and Project	15%+15%
Final Exam	20%
Final Project & Presentation	30%

Programming: Python with the following main tools and modules:

Course Sections

Note: The following table of contents will be progressively updated according to the course advancement.

Topic	Slide	TP	Solution	Remark
Naive Bayes Classifier, LDA & QDA	Slide	TP1	TP1-Solution	✅ Completed
Logistic Regression & Regularization	Slide	TP2	TP2-Solution	✅ Completed
Deep Neural Networks	Slide	TP3	T3-Solution	...Loading
Nonparametric Models	Slide	TP4	TP4-Solution	In progress...

Midterms, Exams and Projects

In this section, you will find all the information related to the midterms, exams and projects including instructions, starting dates and the deadlines.

Resources and Further Reading

Here, you will find additional resources, including books, research papers, and online courses, to further your understanding of advanced machine learning.

📚 You will find these books helpful…