Data analysis (Software Engineering) 2018
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
Final exam will be held in the 22nd of June at 10:30 in room 301.
Questions list is awailable here.
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.
You should send presentations before June 3 23:59. Presentations should be here. On the title page of the presentation you should list all team participants.
Presentation should be in pdf or ppt format and have all components listed in competition rules. Code in py or ipynb format should also be attached to the letter (it may consist of several files).
Lecture 2. Metric methods.
Lecture 3. Decision trees.
Lecture 4. Linear regression. Gradient descent.
Lecture 5. Linear classifiction. Logistic Regression
Lecture 6. Supervised learning quality measures
Lecture 7. Support Vector Machines. Kernel Trick
Lecture 8. Dim. Reduction. PCA, t-SNE
Lecture 9. Ensembles. Bagging, Stacking, Blending
Lecture 10. Ensembles. Boosting
Lecture 11. Neural Networks 1
Lecture 12. Neural Networks 2
Lecture 13. Clustering
Lecture 14. Clustering 2
Lecture 15. Intro to Recsys
Seminar 12. Neural Networks 2
Practice in class
Seminar 14. Kaggle Presentations
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 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!
- Machine learning course from Evgeny Sokolov on Github
- Video-lectures of K. Vorontsov on machine learning
- 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