Magistracy

Speciality Code:

7M07115

Speciality Name:

Machine learning and data mining

Faculty:

Information technology

Qualification:
  • Scientific and pedagogical direction - Master of Technical Sciences
  • Model of graduating student
  • Mandatory disciplines
  • Elective disciplines
  • Professional
ON Use the conceptual apparatus, methods, techniques and technologies for developing software (software) for inductive learning based on the analysis and synthesis of information data flows, which are precedents for the problem being solved. Such sets of tasks characteristic of the banking sector, online trading, IoT, social networks, data measuring devices of complex technical objects (TO), servers DC;
ON 2 Conduct comparative – regression, comparative probabilistic, systemic and structural analysis for modeling and formalization of large information flows of Internet space. To use data mining & information extraction approaches as an alternative to these statistical methods;
ON 3 Solve network technical, economic marketing, banking, information and forecasting tasks based on Knowledge Bases accumulated by expert systems in Data Centers to structure this information into a single, understandable and self-learning formalized mathematical model;
ON 4 Process data streams of servers, maintenance, Internet sources to build a variety of situational objects and a variety of possible responses, the reactions studied, depending on the cause-time development of the system. To be able to solve typical tasks using Google's DeepMind simulator;
ON 5 To correlate the methodological foundations of the analytical approaches of formal mathematics with the concepts of fuzzy logic and the search for implicit solutions by algorithms of neural networks based on the empirical formalization of solutions;
ON 6 Effectively developed self-learning systems for generalizing the various information flows of DC, Internet resources, the testimony of numerous sensors of complex TO to develop an adequate response to data falling outside the limits of a training set of situations;
ON 7 Create new knowledge bases and segments in DC. Design a pilot Machine Learning for maintenance and business processes with the formation of self-learning mathematical models for the processing of large data flows DC KazNU named after Al Farabi;
ON 8 To create projects on the basis of artificial neural networks for deep learning with a teacher, to apply the methods of error correction, back propagation of errors and reference factors;
ON 9 To form pilot courses for training employees of business companies, to conduct trainings on big data, machine learning and interface design. To be able to transparently and clearly present the conceptual framework ML / AI / Big Data and their areas of application;
ON 10 To have the skills to use the application programs of the Machine Learning package in the approximation of functions, handwriting recognition, technical diagnostics;
ON 11 Apply Machine Learning methods to study time series or signals, image or video sequence;
ON 12 Use skills to work with information from various literary sources, present it in various forms of messages, presentations and reports taking into account the specifics of the audience, substantiating and competently presenting their point of view on problematic issues. Effectively work in a team when searching and solving research problems of the OP.
  Data for 2016-2019 years
  Data for 2016-2019 years
  Data for 2016-2019 years