Skip to content

Latest commit

 

History

History
52 lines (39 loc) · 3.39 KB

admission.md

File metadata and controls

52 lines (39 loc) · 3.39 KB
Raw

The 2022 admission to Master's programme

The admission will be anounced in May, June or July at your request to mlalgorithms(at)gmail.com.

Admission format: interview

Algorithm of admission:

  1. A student fills in the form,
  2. solves one of the following tasks,
  3. makes an opinion about the work performed by the students of the department,
  4. on the appointed date, makes a three-minute report on the task and on the topic or work of interest,
  5. is waiting for the decision of the dean's office on the distribution to the department.

The interview procedure from the point of view of the department teachers:

1.Gathering the whole group of those wishing to enter the department, 2. Listen to a short report of each participant,

  • "solving a test problem",
  • "my goals for admission to the department" on the material of student works of the department,
  1. Ask questions, look at the questionnaire,
  2. Draw up a ranked list of applicants, publish [by the link here] and send it to the dean's office, close the reception.

The Master admission procedure:

Процедура приема в магистратуру:

  1. A student fills the form,
  2. cобираем желающих поступить на кафедру по запросам от них,
  3. смотрим анкету, дипломную работу, спрашиваем мнение о теме и работах студентов кафедры, задаем вопросы по математической части бакалаврской программы.

1.Gathering the whole group of those wishing to enter the department 2. Listen to a report about their Bachelor's dissertations, ask questions about

  • BS thesis,
  • topics and results of the students scientific works at the department,
  • mathematics of the BS programme: algebra, analysis, probability (functional analysis and measure theory included)

Test problems (2022 and earlier)

Link – the spring of 2023 will be published here

Check the your basic abilities, spring 2023

Chapters 1, 2, 3 from the Pen and Paper Exercises in Machine Learning by Michael U. Gutmann

Tips for solving problems (these are just tips, not directions)

  • A theoretical solution is more important than a practical one.
  • Write the theory yourself (do not copy the material from the lectures).
  • Visualize problem solving (freehand drawing is preferable to none).
  • Put the results of the experiment on slides (do not show pynb).
  • When discussing neural networks, keep in mind that for us they are not black boxes, but transparent ones.
  • Take these, or past, or your tasks - which is interesting to you.

Report topics for discussion

Link