6B06101 · GROUP B057 · BACHELOR’S
AI and Machine Learning
The core engineering profession of the artificial intelligence era: building, training and deploying intelligent systems that run on data.
3 years
DURATION
240 credits
PROGRAM WORKLOADWORKLOAD
English, Kazakh
LANGUAGE OF INSTRUCTIONLANGUAGE
Full-time, trimesters
MODE OF STUDYMODE
Bachelor in ICT
DEGREE
2 500 000 ₸ per year
TUITION
UNT 70 — fee-paying, 90 — grantUNT 70 / 90
PASS SCORE
20 June — 25 August
ADMISSION 2026–2027
ABOUT THE PROGRAM
If Physical AI is intelligence that moves through the physical world, AI and Machine Learning is the core of the whole modern AI industry: building, training and deploying intelligent algorithms and systems that run on data.
The program trains bachelors able to build, train, evaluate, deploy and maintain intelligent systems for applied and research tasks across very different sectors of the economy: financial technology, healthcare, industry, public administration, education, telecommunications, smart city systems, robotics, data analysis and digital platforms.
The program covers the key areas of modern artificial intelligence — machine learning, deep neural networks, computer vision, natural language processing, generative models — and at the same time pays particular attention to the engineering and industrial side of building AI solutions: MLOps processes, scalable AI services, work with distributed data, and the interpretability, reliability and ethical soundness of intelligent systems.
CAREER
What graduates do
- AI Engineer
- Machine Learning Engineer
- Data Scientist
- AI Researcher
Graduates build AI products, create intelligent services, train artificial intelligence models and bring AI into business, industry and the public sector.
INDUSTRIES
FEATURES
Program features
The full range of modern AI — from classical ML to generative models
Machine learning, deep neural networks, computer vision, natural language processing and generative artificial intelligence — the program covers the entire current technology landscape of the industry.
An industrial focus, not only a research one
Particular attention goes to MLOps, to building scalable AI services and to working with distributed data — to how a model reaches real industrial use.
Responsible AI as an engineering standard
The interpretability, reliability and ethical soundness of intelligent systems are not a separate topic but part of the engineering culture of the program.
Flexible specialization inside the program
Five individual learning tracks let a student go deeper in exactly the area of AI that interests them, supported by elective courses and project work.
A wide range of fields of application
The program prepares specialists for several sectors of the economy at once — fintech, healthcare, industry, public administration, telecommunications and smart city systems.
Recognized by the national professional standards
The program meets the official professional standards of the Atameken National Chamber of Entrepreneurs for developing AI applications and big data processing systems.
A path into research and a master’s degree
The program prepares you not only for a career in industry but also for continuing to a master’s degree and taking part in the research and technology ecosystem of AI.
SPECIALIZATIONS
Educational tracks
The program offers individual learning tracks that let students go deeper in the areas they choose. A track is chosen according to the student’s professional interests and is supported by elective courses and project work.
Building and deploying machine learning and deep learning systems
For those who want to build and train ML/DL models — from basic classification and regression algorithms to advanced neural network architectures.
Data analysis and data science
For those interested in the full data cycle: collection, cleaning, transformation, analysis and visualization with modern data science tools.
Computer vision and multimedia data processing
For those who want to create systems that recognize and interpret images, video and other visual content.
Natural language processing and generative artificial intelligence
For those interested in language models, generative systems and the technology behind modern AI products of the ChatGPT class.
AI systems engineering and MLOps
For those who want not just to train models but to deploy, monitor and maintain them in production — to build the infrastructure around artificial intelligence.
LEARNING OUTCOMES
What you will learn
On completing the program, a graduate is able to:
- Apply critical thinking and the analysis and interpretation of information, formulate tasks and present professional results orally and in writing, and work effectively both alone and in a team.
- Apply fundamental knowledge of mathematics, statistics and probability theory to analyze data, build models and solve artificial intelligence tasks.
- Develop and train basic and advanced machine learning models for classification, regression, clustering and forecasting tasks on prepared datasets.
- Apply deep learning methods, including neural networks, to complex data types — images, text, audio and time series.
- Carry out the full data cycle: collection, cleaning, transformation, analysis and visualization with modern data science tools and technologies.
- Design the architecture of intelligent systems and choose how to integrate artificial intelligence models into applied software solutions and digital services.
- Evaluate the quality, robustness and performance of artificial intelligence models and tune, validate and adapt them to real conditions of use.
- Use modern development tools and platforms, including machine learning libraries and MLOps technologies, to deploy and maintain AI solutions.
- Assess the risks, limitations, security and interpretability of artificial intelligence models and take into account the ethical, legal and social aspects of building and using them.
- Apply interdisciplinary knowledge of programming, economics, entrepreneurship and academic communication when building, analyzing and deploying digital and intelligent solutions.
PRACTICE AND TOOLS
Practice and tools
During their studies students gain hands-on experience of the full cycle of building AI/ML solutions — from defining the task to industrial deployment.
- Python, modern programming languages and development tools
- For building AI/ML solutions
- TensorFlow, PyTorch, Keras
- Machine learning and deep learning libraries and frameworks
- Data tooling
- Collecting, cleaning, processing, storing and visualizing data, including work with databases (MongoDB among them) and building data pipelines
- MLOps tools
- For deploying, monitoring and maintaining models in production
- Methods for processing images, text and other data types
- Computer vision and natural language processing in intelligent systems
- Cloud and high-performance computing for AI
- Building scalable AI services and working with distributed data
THREE STAGES OF PRACTICE
Educational practice
Getting to know the tools of the industry
Professional practice
Working on real tasks
Pre-diploma practice
Delivering a complete AI project on your own
Practice runs in three stages, so a student moves step by step from getting to know the tools of the industry to delivering a complete AI project on their own.
STUDY PLAN
What you study
Courses marked “Elective” are chosen by the student.
| Course Title | Credits |
|---|---|
| YEAR 1 | |
| Trimester 1 | |
| Physical Education | 2 |
| Cultural Studies | 2 |
| Information and Communication Technologies | 5 |
| Foreign Language 1 | 5 |
| History of Kazakhstan (State Exam) | 5 |
| Fundamentals of Calculus | 5 |
| Introduction to Programming | 5 |
| Trimester 2 | |
| Physical Education | 2 |
| Sociology | 2 |
| Foreign Language 2 | 5 |
| Discrete Mathematics for Computing | 5 |
| AI Fundamentals | 4 |
| Object-Oriented Programming | 5 |
| Linear Algebra for Data Science | 5 |
| Trimester 3 | |
| Physical Education | 2 |
| Political Science | 2 |
| Psychology | 2 |
| Applied Calculus | 5 |
| Educational Practice | 2 |
| Data Engineering | 5 |
| Algorithms and Data Structures for Data Science | 5 |
| YEAR 2 | |
| Trimester 4 | |
| Philosophy | 5 |
| Physical Education | 2 |
| Kazakh (Russian) Language 1 | 5 |
| Probability and Data Analysis | 5 |
| Multivariable Calculus | 5 |
| Elective: AI Systems Architecture and Design / Cloud Computing for AI | 5 |
| Trimester 5 | |
| Kazakh (Russian) Language 2 | 5 |
| Optimization for Machine Learning | 5 |
| AI Systems Integration and Deployment | 5 |
| Elective: Monitoring and Reliability of AI Systems / Parallel Data Processing Methods | 5 |
| Machine Learning Systems | 5 |
| Computer Vision | 5 |
| Trimester 6 | |
| Academic Writing | 5 |
| Feature Engineering | 5 |
| MLOps Fundamentals / Model Deployment | 4 |
| Industrial Practice | 4 |
| Advanced Optimization in Deep Learning | 5 |
| Probabilistic Machine Learning | 5 |
| YEAR 3 | |
| Trimester 7 | |
| Elective: Financial Literacy / Digital Entrepreneurship and Startups / Technological Entrepreneurship | 5 |
| Advanced Natural Language Processing | 5 |
| Elective: Generative AI / Quantum Machine Learning | 5 |
| Research Methods and Tools | 5 |
| High Performance Computing for AI | 5 |
| Deep Learning Systems | 5 |
| Trimester 8 | |
| Time Series and Forecasting | 5 |
| Simulation Methods in AI | 5 |
| Elective: AI Product Management / Data Visualization | 5 |
| Elective: Cloud Computing / Data Engineering and Pipelines | 5 |
| Elective: Deep Learning / Reinforcement Learning | 5 |
| Trimester 9 | |
| Industrial Practice | 8 |
| Undergraduate Practice | 4 |
| Writing Diploma Work (Project) and Defence / Comprehensive Examination | 8 |
TUITION AND FUNDING
Tuition and grants
STATE GRANT
Covers the tuition in full
The national grant competition of the Republic of Kazakhstan: a qualifying UNT score, program group B057 “Information Technology”, the QPT and confirmed English.
QAIRU FOUNDER’S GRANT
100% of the tuition, awarded by competition
Three stages of selection: an online application, online selection and the final round at QAIRU. For finalists the university covers accommodation, meals and travel.
ADMISSION
How to apply
Take the UNT
70 points — fee-paying, 90 — the state grant competition. Program group B057 “Information Technology”.
Submit your documents
Registering at admission.qairu.kz takes a few minutes.
Confirm your language
English: IELTS from 5.0, TOEFL iBT from 65 or ITP from 460. Kazakh: KAZTEST at level B1.
Take the QAIRU Potential Test
Online, 35 minutes, 30 questions. The pass score is 23 out of 30.
| REQUIREMENT | VALUE |
|---|---|
| Group of study programs | B057 “Information Technology” |
| Total UNT score | 70 |
| UNT score for the state grant competition | 90 |
| Core UNT subjects | Mathematics — 5, Computer science — 5 |
| English: IELTS Academic | from 5.0 |
| English: TOEFL iBT | from 65 |
| English: TOEFL ITP | from 460 |
| Kazakh: KAZTEST / Qazaq Resmi Test | level B1 |
| Internal testing | QAIRU Potential Test: 35 minutes, 30 questions, pass score 23 |
Get a consultation
We will walk you through the program, the entrance tests and the payment — by phone or by email.
- 8 (700) 300 33 17
- info@nairu.edu.kz
Astana, 55/1 Mangilik El Ave., EXPO Business Center, block B 2.2
Mon–Fri, 09:00–18:00
QUESTIONS AND ANSWERS
Questions and answers
No. The program starts with the fundamentals — mathematics, statistics and programming — and takes you up to advanced neural network architectures.
6B06101 · BACHELOR’S
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