Masinõpe 2020/21 sügis

Course info:

This course aims to give students a very clear intuition behind the most essential Machine Learning methods. Therefore, we will practice implementing some of the key algorithms (like decision trees, backprop, bagging tree) in machine learning, so that it can be done if the need arises. We will discuss and gain practical experience with `sklearn` library. We will build an understanding as to when some algorithms should be preferred to other algorithms.

As a prerequisite to taking this course, the students are expected to be able to program in Python and be familiar with NumPy and Pandas Python libraries. LTAT.03.001 Programming provides sufficient background.

Course schedule:

• The course starts on September 7, 2020 (no lecture and no practice sessions during the first week of September)
• Lectures:
  • Monday 10:15 - 12:00, only online (via [log into courses to see link] )
• Practice Sessions:
  • Group 1: Monday 12:15 - 14:00, Narva mnt 18, room 1019 and (log into courses to see link) (Dmytro)
  • Group 2: Monday 12:15 - 14:00, Narva mnt 18, room 1008 and Zoom (log into courses to see link) (Lisa)
  • Group 3: Wednesday 16:15 - 18:00, Narva mnt 18, room 1008 and Zoom (log into courses to see link) (Mohammed)
  • Group 4: Wednesday 16:15 - 18:00, Narva mnt 18, room 1019 and Zoom (log into courses to see link) (Victor)
  • Group 5: Tuesday 16:15 - 18:00, Narva mnt 18, room 1022 and Zoom (log into courses to see link) (Youssef)
  • Group 6: Tuesday, 16:15 - 18:00, Narva mnt 18, room 2039 and Zoom (log into courses to see link) (Behrad)
• Contacts:
  • Lecturer:
    • Dmytro Fishman (dmytro.fishman@ut.ee)
  • Teaching Assistants:
    • Lisa Yankovskaya (lisa.yankovskaya@ut.ee)
    • Mohammed Ali (mohammed.ali@ut.ee)
    • Youssef Mohamed (youssef.mohamed@ut.ee)
    • Victor Pinheiro (victor.pinheiro@ut.ee)
    • Behrad Moeini (behrad@ut.ee)

Course forum:

Forum and discussions will be held in Piazza. All the registered students will be enrolled by the instructors (you need to confirm your enrollment). If you encounter any problems, then please let us know!

Grading and requirements:

The grade is calculated from the total number of points (max 100). The points can be earned as follows:

• Homeworks (60 points): there will be 6 homeworks, each worth 10 points;
• Paper summary (10 points);
• Project completed in teams (30 points);

Passing criteria

In order to pass the course, the student must have at least 51 point (grade E) in total and get at least 50% from regular exercises and project (30 homeworks points from the regular exercises and 15 points from the project).

Exam

There will be no exam. The final grade is calculated as explained above.

Attendance

Attendance of both lectures and practice sessions is not compulsory. But we encourage students to participate physically if there is a possibility. Please, note the number of students present in a room must not exceed 30, so, please, do not voluntarily attend to other practice sessions, which are you are not registered to.

Deadlines

All deadlines in the course, are strict deadlines. Students have 6 late days in total per semester. Late days will be automatically taken away once the student submits an assignment after the deadline. Generally, 1 minute after the deadline means 1 late day. After the late days are exhausted, each additional late day is -20% from the assignment total.

Plagiarism

All homeworks are checked for plagiarism. If caught first time, we will subtract points for the exercise(s) from the homework total. If caught the second time, formal notification to the Dean's office will be filed.

The course performance will be published using pseudonyms. You can find your pseudonym here.

Course programming language:

Homeworks are required to be solved using Python version 3. Practice sessions, examples and support is given only for that language. We will be using Google Colab during the practice sessions, so it is recommended to get familiar with Colab and also make sure you have Google account.

Links to previous courses:

2019 fall
2018 fall
2018 spring
2017
2016
2015
2013
2012
2008