Official repository for the Machine Learning course at the University of Tehran.
This repository contains course materials, lecture notes, assignments, notebooks, project information, course policies, and additional resources.
| Section | Description |
|---|---|
| Syllabus | Course description, objectives, prerequisites, topics, and grading structure |
| Lectures | Lecture slides, notes, recordings, and related materials |
| Assignments | Homework, programming assignments, submission instructions, and related materials |
| Notebooks | Python notebooks, examples, tutorials, and implementation materials |
| Final Project | Project guidelines, phases, datasets, deadlines, and evaluation criteria |
| Course Policies | Grading, late submissions, collaboration, academic integrity, AI tools, and regrade requests |
| Resources | Textbooks, tutorials, useful links, and additional learning materials |
| Course Staff | Instructor and teaching assistants |
| Item | Details |
|---|---|
| Course Title | Machine Learning |
| University | University of Tehran |
| Faculty / School | To be announced |
| Department | To be announced |
| Semester | To be announced |
| Academic Year | To be announced |
| Instructor | To be announced |
| Course Format | To be announced |
| Communication Channel | To be announced |
| Week | Topic | Lecture Materials | Notebook / Code | Assignment |
|---|---|---|---|---|
| 1 | Introduction to Machine Learning | TBA | TBA | — |
| 2 | Linear Regression | TBA | TBA | TBA |
| 3 | Logistic Regression | TBA | TBA | TBA |
| 4 | Model Evaluation and Regularization | TBA | TBA | TBA |
| 5 | Optimization for Machine Learning | TBA | TBA | TBA |
| 6 | Decision Trees and Ensemble Methods | TBA | TBA | TBA |
| 7 | Support Vector Machines | TBA | TBA | TBA |
| 8 | Unsupervised Learning | TBA | TBA | TBA |
| 9 | Neural Networks | TBA | TBA | TBA |
| 10 | Deep Learning Basics | TBA | TBA | TBA |
| 11 | Convolutional Neural Networks | TBA | TBA | TBA |
| 12 | Sequence Models and Attention | TBA | TBA | TBA |
| 13 | Probabilistic Machine Learning | TBA | TBA | TBA |
| 14 | Review / Project Presentations | TBA | TBA | TBA |
| Assignment | Topic | Release Date | Due Date | Link |
|---|---|---|---|---|
| CA0 | Python / NumPy Warm-up | TBA | TBA | TBA |
| CA1 | Linear Models | TBA | TBA | TBA |
| CA2 | Model Evaluation and Regularization | TBA | TBA | TBA |
| CA3 | Neural Networks | TBA | TBA | TBA |
| Final Project | Applied Machine Learning Project | TBA | TBA | Project |
Course policies will be announced and maintained in the Course Policies section.
The policies will include:
- Grading policy
- Late submission policy
- Collaboration policy
- Academic integrity policy
- AI tools policy
- Regrade request policy
- Communication policy
Information about the instructor and teaching assistants will be available in the Course Staff section.
Enrollment information is maintained by the course staff and will be shared through official university channels.
Student information will not be published publicly unless explicitly approved by the course staff and the university.
Additional learning resources, textbooks, tutorials, and useful links will be available in the Resources section.
.
├── assignments/
├── lectures/
├── notebooks/
├── policies/
├── project/
├── resources/
├── staff/
├── syllabus/
├── .gitignore
└── README.md