Machine Learning and Data Mining
Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Type: Elective course (Data Science)
Area of studies: Applied Mathematics and Informatics
Delivered by: School of Data Analysis and Artificial Intelligence
Where: Faculty of Computer Science
When: 2 year, 1, 2 module
Mode of studies: offline
Open to: students of all HSE University campuses
Instructors: Artem Maevskiy, Alexander Rogachev
Master’s programme: Data Science
ECTS credits: 8
Contact hours: 54
The course "Machine Learning and Data Mining"; introduces students to new and actively evolving interdisciplinary field of modern data analysis. Started as a branch of Artificial Intelligence, it attracted attention of physicists, computer scientists, economists, computational biologists, linguists and others and become a truly interdisciplinary field of study. In spite of the variety of data sources that could be analyzed, objects and attributes that from a particular dataset poses common statistical and structural properties. The interplay between known data and unknown ones give rise to complex pattern structures and machine learning methods that are the focus of the study. In the course we will consider methods of Machine Learning and Data Mining. Special attention will be given to the hands-on practical analysis of the real world datasets using available software tools and modern programming languages and libraries.
- To familiarize students with a new rapidly evolving filed of machine learning and mining, and provide practical knowledge experience in analysis of real world data.
Expected Learning Outcomes
- Students derive the bias-variance decomposition for MSE and “0-1” losses, and show how regularization affects the tradeoff.
- Students explain and utilize the black-box optimization techniques.
- Students explain the concepts of bootstrapping, bagging and boosting, and justify the choice of a particular weak learner for a given aggregating algorithm.
- Students explain the main approaches to graphical probabilistic models and training of them.
- Students explain the relation between linear models and deep neural networks, describe how neural networks are trained, and understand what the role of data scientist is in designing a deep learning solution to a machine learning problem.
- Students know meta-learning approaches.
- Students know the statement of No-Free-Lunch theorems and explain the role of prior knowledge for solving machine learning problems.
- Students understand the principles behind Variational AutoEncoders and implement them.
- Students understand the principles of Generative Adversarial Networks, know which metrics they can optimize and how to regularize them.
- Students use the techniques for working with imbalanced datasets.
- Introduction to Machine Learning and Data Mining, No-Free-Lunch theorems
- Bias-variance decomposition, regularization techniques
- Introduction to meta-algorithms, bootstrap, boosting
- Introduction and overview of deep learning methods
- Deep generative models: Generative Adversarial Networks (GANs)
- Optimization techniques: black-box methods, first order methods
- Miscellaneous topics: imbalanced datasets, importance sampling, one-class classification methods
- Deep generative models: energy-based models, Boltzmann machines and deep belief networks
- Deep generative models: Variational AutoEncoders
- Meta-learning: concept learning, learning how to learn
- 2021/2022 2nd moduleFinal score for the homework: <br /><i>homework score</i> = min [1, ∑<sub>i</sub>x<sub>i</sub>] - penalty, where x<sub>i</sub> is a score for each homework. <br /><br />(Final grade) = 50% × (<i>homework score</i>) + 50% × (<i>exam score</i>).<ul><li>since each homework has a max score of 1 and there are 3 assignments, it will be scaled by 5/3 in this formula;</li><li>max exam score is 10, so it will be scaled by 1/2.</li></ul><br /><i>Final grade</i> = [5/3 ⋅ <i>homework score</i> + 1/2 ⋅ <i>exam score</i>]
Recommended Core Bibliography
- Hall, M., Witten, Ian H., Frank, E. Data Mining: practical machine learning tools and techniques. – 2011. – 664 pp.
- Han, J., Kamber, M., Pei, J. Data Mining: Concepts and Techniques, Third Edition. – Morgan Kaufmann Publishers, 2011. – 740 pp.
- Hastie, T., Tibshirani, R., Friedman, J. The elements of statistical learning: Data Mining, Inference, and Prediction. – Springer, 2009. – 745 pp.
Recommended Additional Bibliography
- Mirkin, B. Core concepts in data analysis: summarization, correlation and visualization. – Springer Science & Business Media, 2011. – 388 pp.