Data analysis (Software Engineering) 2020

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Slack Invite Link
Anonymous feedback form: here
Previous Course Page
Course repo
 Anytask
 Scores



Course description

In this class we consider the main problems of data mining and machine learning: classification, clustering, regression, dimensionality reduction, ranking, collaborative filtering. We will also study mathematical methods and concepts which data analysis is based on as well as formal assumptions behind them and various aspects of their implementation.

A significant attention is given to practical skills of data analysis that will be developed on seminars by studying the Python programming language and relevant libraries for scientific computing.

The knowledge of linear algebra, real analysis and probability theory is required.

The class consists of:

  1. Lectures and seminars
  2. Practical and theoretical homework assignments
  3. A machine learning competition (more information will be available later)
  4. Midterm theoretical colloquium
  5. Final exam

Final Exam

Final exam will be held on the 8th of June

Details about timing will be available soon

Questions list and rules are available here.

For complete instructions please read #general channel in slack

Kaggle

Link to competition is in slack

You should send reports before June 5 23:59 (Competition ends on the 4th of June, late submissions are not considered).
Reports should be sent to the special form

Try to follow the format of report template - https://github.com/shestakoff/hse_se_ml/blob/master/2019/kaggle/kaggle-report-template.ipynb

Colloquium

Colloquium will be held on the 7th of April during seminar

You may not use any materials during colloquium, except single A4 prepared before the exam and handwritten personally by you (from two sides). You will have 2 questions from question list with 15 minutes for preparation and may receive additional questions or tasks.

Course Schedule (3rd module)

Lectures

Mondays

  • 10:30-11:50, Room R205

Lecture materials

Lecture 1. Introduction to data science and machine learning
Slides

Lecture 2. Metric-based methods. K-NN
Slides

Lecture 3. Decision Trees
Slides

Lecture 4. Linear Regression
Slides

Lecture 5. Linear Classification
Slides

Lecture 6. Quality measures
Slides, record

Lecture 7. Dimension reductio. PCA
Slides

Lecture 8. NLP Introduction
Slides

Lecture 9. Word embeddings
Slides

Lecture 10. Ensembles. Random Forest
Slides

Lecture 11. Ensembles. Boosting
Slides

Lecture 12. Neural Networks 1
Slides

Lecture 13. Neural Networks 2
Slides

Lecture 14. Clustering
Slides

Lecture 15. Recsys
Slides

Seminars

Seminar 1. Introduction to Data Analysis in Python
Practice in class
Homework 1 Due Date: 28.01.2020 23:59

Seminar 2. Metric-based methods. K-NN
Practice in class
Homework 2 Due Date: 04.02.2020 23:59

Seminar 3. Decision Trees
Practice in class
Homework 3 Due Date: 01.03.2020 23:59

Seminar 4. Linear Regression
Practice in class

Seminar 5. Logistic Regression
Practice in class
Homework 4 Due Date: 22.03.2020 23:59

Seminar 6. Quality Measures
Practice in class

Seminar 7. Dimention Reduction
Practice in class
Homework 5 Due Date: 12.04.2020 23:59

Seminar 8. Introduction to NLP
Practice in class
Kaggle 1 Due Date: 28.04.2020 23:59

Seminar 9. Word2Vec
Practice in class

Seminar 10. Ensembles. Random Forest
Practice in class
Homework 6 Due Date: 12.05.2020 23:59

Seminar 11. Boosting
Practice in class
Homework 7 Due Date: 19.05.2020 23:59

Seminar 12. NN-1
Practice in class

Seminar 13. NN-2
Practice in class
Homework 8 Due Date: 27.05.2020 23:59

Seminar 14. Clustering
Practice in class

Seminar 15. RecSys
Practice in class

Theoretical questions for the colloquium

Metric-based methods. K-NN
Decision Trees
Linear Regression
Logistic Regression
Quality Measures

Boosting

Evaluation criteria

The course lasts during the 3rd and 4th modules. Knowledge of students is assessed by evaluation of their home assignments and exams. There are two exams during the course – after the 3rd module and after the 4th module respectively. Each of the exams evaluates theoretical knowledge and understanding of the material studied during the respective module.

Grade takes values 4,5,…10. Grades, corresponding to 1,2,3 are assumed unsatisfactory. Exact grades are calculated using the following rule:

  • score ≥ 35% => 4,
  • score ≥ 45% => 5,
  • ...
  • score ≥ 95% => 10,

where score is calculated using the following rule:

score = 0.7 * Scumulative + 0.3 * Sexam2
cumulative score = 0.8 * Shomework + 0.2 * Sexam1 + 0.2 * Scompetition

  • Shomework – proportion of correctly solved homework,
  • Sexam1 – proportion of successfully answered theoretical questions during exam after module 3,
  • Sexam2 – proportion of successfully answered theoretical questions during exam after module 4,
  • Scompetition – score for the competition in machine learning (it's also from 0 to 1).

Participation in machine learning competition is optional and can give students extra points.
"Automative" passing of the course based on cumulative score may be issued.

Kaggle competition 1
«Score» = ("your quality"-"baseline method quality") / ("max achieved quality" - "baseline method quality")
Required condition: a notebook with your best solution must be reproducible. Otherwise, you will not get any score.

Plagiarism

In case of discovered plagiarism zero points will be set for the home assignemets - for both works, which were found to be identical. In case of repeated plagiarism by one and the same person a report to the dean will be made.

Deadlines

Assignments sent after late deadlines will not be scored (assigned with zero score) in the absence of legitimate reasons for late submission which do not include high load on other classes.

Structure of emails and homework submissions

Practical assignments must be implemented in Jupyter Notebook format, theoretical ones in pdf. Practical assignments must use Python 3 (or Python 3 compatible). Use your surname as a filename for assignments (e.g. Ivanov.ipynb). Do not archive your assignments.

Assignments can be performed in either Russian or English.

Assignments can be submitted only once!

Link for the submissions: Anytask.

Useful links

Machine learning, Stats, Maths

Python

Python installation and configuration

anaconda

Источник — «http://wiki.cs.hse.ru/index.php?title=Data_analysis_(Software_Engineering)_2020&oldid=41694»