Course schedule

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This course introduces to the technologies of scientific research, to teach the students to present the results of their studies in the format that is acknowledged by other researchers from the field of Machine Learning and Data Analysis. The expected result of the course is a research paper, submitted to a peer-reviewed journal from the list of the Higher Attestation Commission.

  • A student is willing to learn to formally state research problems, find adequate references, generate novel and significant ideas for problem solving.
  • An advisor helps the student with technical issues, consults the student on topics of machine learning, promptly reacts to arising problems, performs evaluations and grading. Each advisor is supposed to possess sufficient publishing experience. Ideally, the advisor is writing paper on the adjacent topic. It is recommended to organize weekly reviewing process in such way that a student would input the corrections himself.
  • An expert guarantees novelty and importance of the paper, suggests the problems, provides data.

Goals

  • General: to learn how to convey author's message to the reader in a clear way.
  • Practical: to publish a scientific paper, to be welcome in the research society.

Delivery

  1. Research paper in a peer-reviewed scientific journal
  2. Computational experiment with analysis and code to reproduce it
  3. Slides with a brief comprehensive results
  4. Video of the presentation speech

Schedule, Spring 2021

Date N To be done Result to discuss Symbol
February 11 1 Set the workflow, schedule, tools. List of participants. Subscribed to the schedule
18 2 Select a project. Tools are ready to use. The project initial status is set. Set the record
25 3 List references, write Abstract, Introduction, LinkReview. Abstract, Introduction, References in bib-file. Abstract, Introduction, Literature
March 4 4 State a problem, write a description of your basic algorithm, prepare your computational experiment. Write the problem statement, write the basic algorithm description. Problem statement
11 5 Set goals and plan report of your computational experiment. Run basic code. Write down results. Goals of the experiment. Basic code, draft report on the basic algorithm. Ready to the first checkpoint. Update, eXperiment palning, Basic code, Report, cHeck-1
18 6 Set your computational experiment using proposed algorithm and your previous results. Code, visual presentation of results, error and quality analysis. Create a draft of your presentation for 2-3 minutes. Code, Visualization
25 7 Describe the algorithm. The theory and and algorithms of the paper. Theory
April 1 8 Finalise the computational experiment. The experiment description with error analysis. Error
8 9 Prepare the theoretical part and computational experiment. Explain the figures, write conclusions. Ready to the second checkpoint. The paper draft with the sections Computational experiment and Conclusions. Checkpoint. Document, cHeck-2
15 10 The paper is ready for Review. Your paper is ready to the peer-review. RevieW
22 11 Finalization The paper and slides are subjects to submit. Journal, Slides
29 12 Prepare your presentation. Presentation day. Final show

Consultations

  1. The workflow goes around each week, see Todo list.
  2. The iterative consultations and delivery of results are highly welcome! Start during the weekends.
  3. Deadline of the last version is: Wednesday 6:00am. The review goes on Wednesday working day.
  4. Each symbol A gives +1 according the system (А-, А, А+). No symbol gives A0.

Workload

  1. Student's workload depends on the group and can vary from 54 to 200 hours
    • The group of the MIPT Intelligent Systems Department is 74-128 hours
    • The group of the MIPT Faculty of Innovation and Technologies is 200 hours (expended software system and deployment part).
  2. Consultants are expected to make one-hour meeting weekly and promptly to student's questions. So it makes 12 to 16 hours.
  3. Expert is expected to state the problem and evaluate the delivery. It makes negative workload: for a problem the expert solves as a daily routine some delivery appears after several months of synchronized work. The quality of the stated problem matters.