Data Mining and Elements of Machine Learning
- The purpose of the course is to familiarize students with the basic principles and methods of data analysis and machine learning.
- Trains logistic regression and KNN, understand quality metrics.
- Trains classification based on decision trees and ensemble models
- Trains the model of classification based on SVM and various parameters
- Trains clustering models, understands clustering evaluation
- Performs a spectrum of machine learning tasks
- Reduces the dimensionality with various methods
- Trains polynomial regression and understand its quality metrics, to identify overfitting and underfitting, to estimate quality during cross-validation
- Trains polynomial regression and understand its quality metrics, identifies overfitting and underfitting, estimates quality during cross-validation
- Trains linear regression, understands its quality metrics
- Prepares data for machine learning algorithms
- Independently conducts a reproducible experiment by a full pipeline: 1) formulate a problem, analyze previous work and scientific papers on the subject; 2) perform preliminary dataset analysis, data preprocessing, feature engineering and selection; 3) select machine learning methods, train, evaluate and compare models; 4) visualize and explain the results.
- Introduction. Examples of practical tasks
- Exploratory data analysis
- Linear regression
- Polynomial regression. The concept of overfitting and regularization
- Classification problem. Logistic regression. The KNN algorithm. Naïve Bayes Classifier.
- Classification algorithms: decision trees and ensembles.
- Support vector machines
- Machine Learning approaches to Named Entities Recognition.
- Unsupervised machine learning tasks. Dimension reduction.
- Unsupervised machine learning tasks. The task of clustering
- Topic Modelling
- 2022/2023 4th module0.1 * Class work + 0.2 * Homework + 0.3 * Exam + 0.4 * Practical project
- Sarkar, D., Bali, R., & Sharma, T. (2018). Practical Machine Learning with Python : A Problem-Solver’s Guide to Building Real-World Intelligent Systems. [United States]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1667293
- Müller, A. C., & Guido, S. (2017). Introduction to Machine Learning with Python : A Guide for Data Scientists: Vol. First edition. Reilly - O’Reilly Media.