The aim of this course is to give students from fields other than data science a hands-on introduction to machine learning. The course is based on short micro-lectures and Python-based homeworks, culminating in a final Kaggle-style competition. The goal is not to build theoretical depth, but to provide practical experience applying key machine learning methods to various datasets. By the end of the course, students should be familiar with: - Data manipulation in Python - Common machine learning techniques - Evaluation of machine learning model

Course Information:

The course will start on 9 February 2026. and the course is weekly based on microlectures. Students are required to view the microlectures and understand the basic concepts about machine learning. the links to microlectures for each week is provided under Lectures section.

• Homework deadlines: Friday at noon (12:00)

Contact:

For Discussion:

• Course Forum: https://piazza.com/ut.ee/spring2026/ltat02040

We will use Piazza for questions and discussions. In the forum, you can post questions about homework or course organization etc.
1. Register here
(Please use the address that is in the study information system SIS/ÕIS).
2. Put the class access code, which is "gqg0s8syg4k".
3. Select the option "join as student". and click join classes button.
4. Enter your ut email and confirm it.
Once you login to your account, you have to fill in some information about yourself, which is a little annoying, but do it anyway. The home page of the course forum is here and you can click on Q&A to get to the forum part. That's it.
In case of any issue, dont hesistate to send the email to TAs :)

For homeworks:

Moodle will be used for homeworks and their gradings. Following is the link to the moodle for this course:
https://moodle.ut.ee/course/view.php?id=14650

• Lecturer:
  Radwa Mohamed El Emam El Shawi (radwa.elshawi@ut.ee)
• Teaching Assistants:
  Mehak Mushtaq Malik (mehak1@ut.ee)
  Vikash Maheshwari (maheshwari@ut.ee)

Grading Requirements:

Each homework is graded on a pass/fail basis.
Students are allowed up to two re-submissions per semester
Students must pass at least 6 out of 8 homeworks to meet the course requirements.
Homework must be submitted on time and must meet the requirements.

Kaggle Competition:Submission of the competition entry is mandatory.
Group project and presentation at the poster session is also mandatory.