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Адрес: 109028, г. Москва, Покровский бульвар, д. 11, корпус S, комната S938 (станции метро "Чистые пруды" и "Курская").
Телефон: +7(495) 772-95-90 *27319
Департамент анализа данных и искусственного интеллекта был создан в 2014 году на базе кафедры анализа данных и искусственного интеллекта. В его состав входят исследователи с мировым именем, активно участвующие в международных исследовательских проектах.
Acquaye F. L., Kertesz-Farkas A., Stafford Noble W.
Journal of Proteome Research. 2023. Vol. 22. No. 2. P. 577-584.
Vasilii A. Gromov, Yury N. Beschastnov, Korney K. Tomashchuk.
PeerJ Computer Science. 2023. Vol. 9. No. .
Kanovich M., Kuznetsov S., Scedrov A.
Information and Computation. 2022. Vol. 287.
Egurnov D., Ignatov D. I.
Automation and Remote Control. 2022. Vol. 83. No. 6. P. 894-902.
Egurnov D., Точилкин Д. С., Ignatov D. I.
In bk.: Complex Data Analytics with Formal Concept Analysis. Springer, 2022. P. 239-258.
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.
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.
Dudyrev E., Semenkov Ilia, Kuznetsov S. et al.
Plos One. 2022. Vol. 17. No. 10.
Stochastic Modelling
Outline of the course
1. Clustering
Lecture Notes 1 Problem sheet 1 Solutions 1
2. PCA / Probabilistic PCA
Lecture Notes 2 Problem sheet 2 Solutions 2
3. Kalman Filtering
Lecture Notes 3 Problem sheet 3 Solutions 3
4. Sampling Methods and Particle Filtering
Lecture Notes 4 Problem sheet 4 Solutions 4
5. Poisson Process
Lecture Notes 5 Problem sheet 5 Solutions 5
Assessment for Module 1
Assignment 1 - till Friday 21st October 2016 at 3.10pm.
Assignment 1 Solution
It is worth 10% of your final mark.
One mid-term exam, worth 25% of your final mark Instructions for the test
Main Reference
[1] Christopher Bishop. Pattern Recognition and Machine Learning, Springer, 2006.
Optional Reading (not examinable)
[1] D. Arthur. and S. Vassilvitskii (2007). k-means++: the advantages of careful seeding. In ACM-SIAM Symposium on Discrete Algorithms. pdf.
[2] C. Burges (2009). Dimension Reduction: A Guided Tour. In Foundations and Trends in Machine Learning. Vol 2(4), pp 275-365. pdf.