Kyrgyz National Technical University

Bachelor of Science in Artificial Intelligence

Bachelor of Science in Artificial Intelligence provides systematic undergraduate preparation in Artificial Intelligence. This field covers machine learning, deep learning, large models, intelligent systems, and AI engineering applications, developing data modeling, algorithm implementation, system integration, and responsible AI capability.

Bachelor of Science in Artificial Intelligence

Bachelor of Science in Artificial Intelligence

Bachelor of Science in Artificial Intelligence provides systematic undergraduate preparation in Artificial Intelligence. This field covers machine learning, deep learning, large models, intelligent systems, and AI engineering applications, developing data modeling, algorithm implementation, system integration, and responsible AI capability.

LevelBachelor's Degree (4 Years)
Program FieldArtificial Intelligence
Award TitleBachelor of Science in Artificial Intelligence

Program Positioning

The bachelor's program provides full undergraduate preparation with emphasis on professional theory, laboratory practice, integrated design, and standard communication. This field covers machine learning, deep learning, large models, intelligent systems, and AI engineering applications, developing data modeling, algorithm implementation, system integration, and responsible AI capability.

Machine Learning Foundations

Study supervised learning, unsupervised learning, feature engineering, model training, and performance evaluation.

Deep Learning and Large Models

Understand neural networks, deep learning frameworks, natural language processing, computer vision, and large-model applications.

Data and Intelligent Systems

Practice data collection, cleaning, annotation, knowledge representation, intelligent application design, and system integration.

AI Engineering and Ethics

Develop skills in model deployment, testing, monitoring, privacy protection, algorithmic bias, and responsible AI practice.

Learning Objectives

Build foundations in AI theory, algorithms, and engineering practice.
Use data and models to solve practical problems.
Develop awareness of intelligent system development, evaluation, and compliant use.

Study and Completion Requirements

  1. Complete general education, major core courses, electives, and practical learning components.
  2. Develop applied capability through course design, laboratory work, case analysis, or engineering projects.
  3. Complete a capstone design, thesis, or equivalent integrated academic and practical outcome.

Learning Outcomes

Machine learning modeling and algorithm implementation
Intelligent application development and system integration
AI project evaluation, governance, and continuous improvement