Data analysis (Software Engineering) 2017
Class email: cshse.ml@gmail.com
Anonymous feedback form: here
Scores: here
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
Kaggle competition
Follow this link to participate.
Baseline loss: 0.89
Colloquium
Colloquium will be held on April 7th during lecture & seminars time slot.
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 the questions list with 25 minutes for preparation and may receive additional questions or tasks.
Syllabus
- Introduction to machine learning.
- K-nearest neighbours classification and regression. Extensions. Optimization techniques.
- Decision tree methods.
- Bayesian decision theory. Model evaluation:
- Linear classification methods. Adding regularization to linear methods.
- Regression.
- Kernel generalization of standard methods.
- Neural networks.
- Ensemble methods: bagging, boosting, etc.
- Feature selection.
- Feature extraction
- EM algorithm. Density estimation using mixtures.
- Clustering
- Collaborative filtering
- 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
Lecture 5. Properties of convex functions. Download
Lecture 6. Linear methods of classification. Download
Lecture 7. Classifier evaluation. Download
Lecture 8. SVM and kernel trick. Download
Lecture 9. Principal component analysis. Download
Lecture 10. Singular value decomposition. Download
Lecture 11. Feature selection. Download
Lecture 12. Working with text. Download
Lecture 13. Ensemble methods. Download
Lecture 14. Boosting. Download
Lecture 15. Neural networks. Download
Seminars
Seminar 1. Introduction to Data Analysis in Python
Practical task 1, data. Deadline: January 19.
Seminar 2. Metric Classifiers
Theoretical task 2, Deadline: January 26
Practical task 2, data. Deadline: February 2
Seminar 3. Decision trees
Theoretical task 3, Deadline: February 2
Seminar 4. Regression methods
Theoretical task 4, Deadline: February 9
Seminar 5. Linear classification: loss functions
Theoretical task 5, Deadline: February 16
Seminar 6. Linear classification: optimization
Theoretical task 6, Deadline: March 2
Practical task 6, first dataset, diabetes dataset. Deadline: March 16
Seminar 7. Classifier evaluation
Theoretical task 7, Deadline: March 16
Seminar 8. SVM and kernel trick
Theoretical task 8, Deadline: March 23
Practical task 8, data. Deadline: March 30
Seminar 9. PCA
Theoretical task 9, Deadline: April 20
Seminar 10. Feature selection + text mining
Theoretical task 10, Deadline: April 27
Seminar 11. Ensembles, bagging
Practical task 11, Deadline: May 18
Seminar 12. Ensembles, boosting
Theoretical task 12, Deadline: May 18
Seminar 13. Neural networks
Practical task 13, Data, Short training set, Deadline: June 1
Additional materials: Backpropagation, PyBrain’s documentation, PyBrain example from the seminar
By default, you should use the whole training set from Data. But if you have MemoryError then use Short training set.
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).
If you solve the theoretical problem in class you obtain 1.5 points (if you solve it at home you obtain 1 point). 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
- 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