Data analysis (Software Engineering) 2019
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:
- Lectures and seminars
- Practical and theoretical homework assignments
- A machine learning competition (more information will be available later)
- Midterm theoretical colloquium
- Final exam
Course Schedule (3rd module)
Dates: Mondays (21.01, 28.01, 04.02, 11.02, 18.02, 25.02, 04.03, 11.03)
- Group BPI-161, 10:30-11:50, Room 507
- Group BPI-162, 12:10-13:30, Room 311
- Group BPI-163, 13:40-15:00, Room 435
Dates: Tuesdays (15.01, 22.01, 29.01, 05.02, 12.02, 19.02, 12.03, 19.03)
- 9:00-10:20, Room 317
Lecture 1. Introduction to data science and machine learning
Lecture 2. Cross-validation. Metric-based models. KNN
Lecture 3. Decision Trees
The course lasts during the 3rd and 4th modules. Knowledge of students is assessed by evaluation of their home assignments and exams. Home assignments divide into theoretical tasks and practical tasks. 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.
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.
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!
- Machine learning course from Evgeny Sokolov on Github
- Video-lectures of K. Vorontsov on machine learning
- Some books for ML1
- Some books for ML2
- On of the classic ML books. Elements of Statistical Learning (Trevor Hastie, Robert Tibshirani, Jerome Friedman)
- Official website
- Libraries: NumPy, Pandas, SciKit-Learn, Matplotlib.
- A little example for the begginers: краткое руководство с примерами по Python 2
- Python from scratch: A Crash Course in Python for Scientists
- Lectures Scientific Python
- A book: Wes McKinney «Python for Data Analysis»
- Коллекция интересных IPython ноутбуков