Data analysis (Software Engineering) 2018
Class email: cshse.ml@gmail.com
Anonymous feedback form: here
Scores
Previous Course Page
Course repo
Telegram Group
Содержание
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:
- Lectures and seminars
- Practical and theoretical homework assignments
- A machine learning competition (more information will be available later)
- Midterm theoretical colloquium
- Final exam
Colloquium
Colloquium will be held on the 6th of April.
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 20 minutes for preparation and may receive additional questions or tasks.
Lecture materials
Lecture 1. Introduction to data science and machine learning.
Slides
Additional materials: 1, 2
Lecture 2. Metric methods.
Slides
Lecture 3. Decision trees.
Slides
Lecture 4. Linear regression. Gradient descent.
Slides
Lecture 5. Linear classifiction. Logistic Regression
Slides
Lecture 6. Supervised learning quality measures
Slides
Lecture 7. Support Vector Machines. Kernel Trick
Slides
Lecture 8. Dim. Reduction. PCA, t-SNE
Slides
Lecture 9. Ensembles. Bagging, Stacking, Blending
Slides
Seminars
Seminar 1. Introduction to Data Analysis in Python
Python Intro, NumPy Tutorial, Pandas Tutorial
Complete tutorials by 28th of January 23:59 and submit an achive (with your name on it) to this link
Seminar 2. Metric Methods
Practice in class, partially filled
Theoretical task 1. Due date: February 2 23:59
Seminar 3. Decision Trees
Practice in class, partially filled
Theoretical task 2. Due date: February **10** 23:59
Practical task 2, dataset Due date: February 17 23:59
Seminar 4. Linear regression
Practice in class, partially filled
Theoretical task 3. Due date: February 17 23:59
Seminar 5. Linear classification
Practice in class, partially filled
Theoretical task 4. Due date: February 25 23:59
Practical task 3, first_dataset, diabetes. Due date: March 11 23:59
Seminar 6. Quality measures
Practice in class, partially filled
Theoretical task 5. Due date: March 11 23:59
Seminar 7. SVM
Practice in class, partially filled
Theoretical task 6. Due date: March 25 23:59
Seminar 8.1. Feature selection
Practice in class, partially filled
No theoretical task
Seminar 8.2. PCA, t-SNE
Practice in class, partially filled
Theoretical task 7. Due date: April 23 23:59
Seminar 9. Ensembles. Bagging, Stacking
Practice in class, partially filled
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 = 0.7 * S_{cumulative} + 0.3 * S_{exam2}
cumulative score = 0.8 * S_{homework} + 0.2 * S_{exam1} + 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.
"Automative" passing of the course based on cumulative score may be issued.
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 2 (or Python 2 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!
Useful links
Machine learning
- Machine learning course from Evgeny Sokolov on Github
- 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