Deep learning methods

Zhomartkyzy Gulnaz

The instructor profile

Description: The discipline aims to study the principles of deep learning for the development of complex intelligent systems, design and application of deep learning neural network models, application of multilayer neural network architecture for specific subject areas, methods and tools of artificial intelligence for solving production IT problems.

Amount of credits: 5

Пререквизиты:

  • Machine Learning and Data Analysis

Course Workload:

Types of classes hours
Lectures 15
Practical works
Laboratory works 30
SAWTG (Student Autonomous Work under Teacher Guidance) 75
SAW (Student autonomous work) 30
Form of final control Exam
Final assessment method

Component: Component by selection

Cycle: Profiling disciplines

Goal
  • To develop doctoral students' knowledge and skills in the field of design, implementation and optimization of neural networks for solving artificial intelligence problems, including computer vision, natural language processing and forecasting.
Learning outcome: knowledge and understanding
  • Knowledge of key concepts and terms, Understanding of neural network architecture, Optimization and regularization of deep learning models, Develop and test models to solve problems in various domains.
Learning outcome: applying knowledge and understanding
  • application of deep learning methods and various models based on different neural network architectures.
Learning outcome: formation of judgments
  • The ability to assess the quality, reliability and relevance of data, to present ideas, to argue and prove logically, the ability to draw conclusions and make informed decisions.
Learning outcome: communicative abilities
  • Ability to clearly and structured describe the completed stages of work, the methods used and the results obtained, answer questions, and argue your position based on data.
Learning outcome: learning skills or learning abilities
  • Ability to analyze various sources of information, ability to master new approaches, methods and tools for data analysis.