Data analysis (Software Engineering) 2017 — различия между версиями

Версия 12:19, 2 февраля 2017

Class email: cshse.ml@gmail.com
Anonymous feedback form: here
Scores: here

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

Syllabus

1. Introduction to machine learning.
2. K-nearest neighbours classification and regression. Extensions. Optimization techniques.
3. Decision tree methods.
4. Bayesian decision theory. Model evaluation:
5. Linear classification methods. Adding regularization to linear methods.
6. Regression.
7. Kernel generalization of standard methods.
8. Neural networks.
9. Ensemble methods: bagging, boosting, etc.
10. Feature selection.
11. Feature extraction
12. EM algorithm. Density estimation using mixtures.
13. Clustering
14. Collaborative filtering
15. Ranking

Lecture materials

Lecture 1. Introduction to data science and machine learning. Download

Lecture 2. Metric methods of classification & regression. Download

Lecture 3. Decision trees. Download

Lecture 4. Regression methods. Download

Seminars

Seminar 1. Introduction to Data Analysis in Python

Additional materials: 1, 2

Practical task 1, data. Deadline: January 19.

Seminar 2. Metric Classifiers

Theory task 2, Deadline: January 26

Practical task 2, data. Deadline: February 2

Seminar 3. Decision trees

Theory task 3, Deadline: February 2

Evaluation criteria

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 = 0.6 * S_homework + 0.2 * S_exam1 + 0.2 * S_exam2 + 0.2 * S_competition

• S_homework – proportion of correctly solved homework,
• S_exam1 – proportion of successfully answered theoretical questions during exam after module 3,
• S_exam2 – proportion of successfully answered theoretical questions during exam after module 4,
• S_competition – 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.

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

Standard period for working on a homework assignment is 2 weeks for practical assignments and 1 week for theoretical ones. The first practical assignment is an exception. 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.

Deadline time: 23:59 of the day before seminar (Thursday).

Structure of emails and homework submissions

All the questions and submissions must be addressed to cshse.ml@gmail.com. The following subjects must be used:

• For questions (general, regarding assignments, etc): "Question - Surname Name - Group"
• For homework submissions: "Practice/Theory {Lab number} - Surname Name - Group"

Example: Practice 1 - Ivanov Ivan - 141

Please do not mix two different topics in a single email such as theoretical and practical assignments etc. When replying, please use the same thread (i.e. reply to the same email).

Practical assignments must be implemented in jupyter notebook format, theoretical ones in pdf. Practical assignments must use Python 3. 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!

Useful links

Machine learning

• machinelearning.ru
• Video-lectures of K. Vorontsov on machine learning
• On of the classic ML books. Elements of Statistical Learning (Trevor Hastie, Robert Tibshirani, Jerome Friedman)

Python

• 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

Python installation and configuration

anaconda