2023/2024
Глубинный анализ данных
Статус:
Маго-лего
Когда читается:
3 модуль
Охват аудитории:
для своего кампуса
Преподаватели:
Карпов Илья Андреевич
Язык:
английский
Кредиты:
3
Контактные часы:
40
Course Syllabus
Abstract
Covers topics in data mining, including visualization techniques, elements of machine learning theory, classification and regression trees, Generalized Linear Models, Spline approach, and other related topics.
Learning Objectives
- The course gives students an important foundation to develop and conduct their own research as well as to evaluate research of others.
Expected Learning Outcomes
- Be able to compare mining diverse patterns, including methods for mining multi-level, multi-dimensional patterns, qualitative patterns,
- Be able to compare negative correlations, compressed and redundancy-aware top-k patterns, and mining long (colossal) patterns.
- Be able to compare pattern evaluation issues, especially several popularly used measures, such as lift, chisquare, cosine, Jaccard, and Kulczynski, and their comparative strengths.
- Be able to recall important pattern discovery concepts, methods, and applications, in particular, the basic concepts of pattern discovery, such as frequent pattern, closed pattern, max-pattern, and association rules.
- Know constraint-based pattern mining, including methods for pushing different kinds of constraints, such as data and pattern-based constraints, anti-monotone, monotone, succinct, convertible, and multiple constraints.
- Know efficient pattern mining methods, such as Apriori, ECLAT, and FPgrowth.
- Know various pattern mining applications, such as mining spatiotemporal and trajectory patterns and mining quality phrases.
- Know well-known sequential pattern mining methods, including methods for mining sequential patterns, such as GSP, SPADE, PrefixSpan, and CloSpan
Course Contents
- 1. Visualizations (and Getting to Know Orange)
- 2. Introduction to predictive modelling
- 3. Model Perfomance
- 4. Linear models for classification
- 5. Another models for classification
- 6. Regularization
- 7. Clustering
- 8. Text Mining
- 9. Projections
- 10. Embeddings
Bibliography
Recommended Core Bibliography
- ElAtia, S., Ipperciel, D., & Zaiane, O. R. (2017). Data Mining and Learning Analytics : Applications in Educational Research. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1351385
- Han, J., & Kamber, M. (2011). Data Mining: Concepts and Techniques (Vol. 3rd ed). Burlington, MA: Morgan Kaufmann. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=377411
- Larose, D. T., & Larose, C. D. (2015). Data Mining and Predictive Analytics. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=958471
- S. K. Mourya, & Shalu Gupta. (2013). Data Mining and Data Warehousing. [N.p.]: Alpha Science Internation Limited. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1688519
Recommended Additional Bibliography
- Brown, M. S. (2014). Data Mining For Dummies. Hoboken: For Dummies. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=842663
- Knobbe, A. J. (2006). Multi-relational Data Mining. Amsterdam: IOS Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=176061
- Motoda, H. (2002). Active Mining : New Directions of Data Mining. Amsterdam: IOS Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=87558