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 | 30% |
| Midterm Exam | 30% |
| Final Project | 30% |
Programming:
Pythonwith the following main tools and modules:
Jupyter Notebook
Google colab
Scikit-learn
PytorchCourse Sections
| Topic | Slide | TP | Solution | Remark |
|---|---|---|---|---|
| Intro to EDA & Univariate Analysis | Slide | TP1 | TP1-Solution | ...Loading |
| Bivariate Analysis | Slide | TP2 | TP2-Solution | ...Loading |
| Data Quality & Preprocessing | Slide | TP3 | TP3-Solution | ...Loading |
| Naive Bayes & Logistic Regression | Slide | TP4 | TP4-Solution< | ...Loading |
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.
π You will find these books helpfulβ¦
