School of Data Analysis and Artificial Intelligence

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The School of Data Analysis and Artificial Intelligence was created in 2014 as part of the Department of Data Analysis and Artificial Intelligence. The school consists of world-renowned researchers who actively participate in international research projects.

Publications

Article

Efficient indexing of peptides for database search using Tide

Acquaye F. L., Kertesz-Farkas A., Stafford Noble W.

Journal of Proteome Research. 2023. Vol. 22. No. 2. P. 577-584.

Article

Generalized relational tensors for chaotic time series

Vasilii A. Gromov, Yury N. Beschastnov, Korney K. Tomashchuk.

PeerJ Computer Science. 2023. Vol. 9. No. .

Article

Mint: MDL-based approach for Mining INTeresting Numerical Pattern Sets

Makhalova T., Kuznetsov S., Napoli A.

Data Mining and Knowledge Discovery. 2022. P. 108-145.

Article

Human knowledge models: Learning applied knowledge from the data

Dudyrev E., Semenkov Ilia, Kuznetsov S. et al.

Plos One. 2022. Vol. 17. No. 10.

Book chapter

Modeling Generalization in Domain Taxonomies Using a Maximum Likelihood Criterion

Zhirayr Hayrapetyan, Nascimento S., Trevor F. et al.

In bk.: Information Systems and Technologies: WorldCIST 2022, Volume 2. Iss. 469. Springer, 2022. P. 141-147.

Book chapter

Ontology-Controlled Automated Cumulative Scaffolding for Personalized Adaptive Learning

Dudyrev F., Neznanov A., Anisimova K.

In bk.: Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium -23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings, Part II. Springer, 2022. P. 436-439.

Book chapter

Triclustering in Big Data Setting

Egurnov D., Точилкин Д. С., Ignatov D. I.

In bk.: Complex Data Analytics with Formal Concept Analysis. Springer, 2022. P. 239-258.

Article

Triclusters of Close Values for the Analysis of 3D Data

Egurnov D., Ignatov D. I.

Automation and Remote Control. 2022. Vol. 83. No. 6. P. 894-902.

Article

Deep Convolutional Neural Networks Help Scoring Tandem Mass Spectrometry Data in Database-Searching Approaches

Kudriavtseva P., Kashkinov M., Kertész-Farkas A.

Journal of Proteome Research. 2021. Vol. 20. No. 10. P. 4708-4717.

Article

Language models for some extensions of the Lambek calculus

Kanovich M., Kuznetsov S., Scedrov A.

Information and Computation. 2022. Vol. 287.

All publications

Delivered by:: School of Data Analysis and Artificial Intelligence

Type:: Mago-Lego

When:: 1, 2 module

Instructor

Sohrabi, Majid

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Abstract

This course serves as an introduction to Data Analytics and Mining, offering participants a foundational understanding of essential concepts and techniques in the field. During this course, students become acquainted with math concepts related to data science and master basic methods of collecting, processing, and transforming data using Python. The curriculum covers fundamental principles such as data preprocessing, visualization, classical machine learning methods, and deep neural networks. Topics include basics of classification methods, regression, image recognition, and natural language processing. Emphasizing practical skills, the course explores Python as a primary tool due to its accessibility and widespread use in data analysis. Geared towards beginners, it covers fundamental principles including data importation, storage, manipulation, and basic analytical methods. Designed to accommodate students with python programming experience, the course serves as a springboard into more specialized areas within Data Analytics and Mining, such as machine learning, statistical data processing, and data visualization.

Data Analytics and Mining

Instructor

Course Syllabus

Abstract