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Artificial Intelligence MSc at BME

A full-time master's program taught in English. You learn to understand, build and use AI systems: machine learning, deep learning, reinforcement learning, generative AI and language models, multi-agent systems, and the engineering needed to run them in production.

Explore the curriculumHow to apply
Degree
Master in Artificial Intelligence
Length
4 semesters
Credits
120
Language
English only
Mode
Full-time, Budapest
Start
September, or February (from 2027)

What the 120 credits are made of

10Mathematics

Two subjects, 5 credits each

30Core AI

Six subjects, 5 credits each

44Specialization

Business, ethics and law (9), five electives (25), project laboratory (10)

6Free choice

Three subjects, 2 credits each

30Thesis

Over two semesters

What you will be able to do

You will design, build and run AI systems. You will also know why they work, where they fail and what the rules around them are. The faculty's AI committee planned the program, and it builds on the faculty's earlier work in human-centered AI education (the HCAIM project).

  • Machine learning, deep learning and reinforcement learning

    The methods behind today's systems, taught with their mathematics. You can choose and adapt them, not only call a library.

  • Generative AI and language models

    Transformers, foundation models, retrieval-augmented generation and agents, from theory to working applications.

  • Multi-agent systems and data processing

    Systems where several learners or robots act together, and the data work every real project needs.

  • Mathematical and statistical foundations

    Linear algebra, optimization, probability and Bayesian methods as the common language of all subjects.

  • MLOps, DevOps and MLSecOps

    Deploying models, keeping them running, and keeping them secure.

  • Ethics, law and business

    Responsible AI, the EU AI Act, and how AI creates value in organizations.

After the degree you can work in industry or continue to doctoral studies.

The curriculum, in one picture

New to the field? Read the map from semester 1 to semester 4. Mathematics and the core subjects give you the language and the methods. Electives let you choose a direction. Everything leads to a thesis of your own.

  • Language

    Linear algebra, optimization and probability. Everything else is written in it.

  • Method

    Machine learning, deep learning and the tools to build and secure real systems.

  • Direction

    Five electives out of sixteen, plus ethics, law and business.

  • Proof

    Project laboratory, a six-week internship and a thesis you defend.

Curriculum map

The order of the subjects depends on whether you start in autumn or in spring. Select a block to see what it is for and how it connects.

Every semester has 30 credits. An arrow joins a subject to an earlier subject whose content it builds on. The arrows are a guide for newcomers, not formal prerequisite rules. Check each subject sheet for its own requirements.

  • Professional internship, 6 weeks, no creditsYou need it for the degree. Recommended in semester 3.
  • Mobility windowSemester 3 is the window for studying abroad.
Show the sample plans as tables

Autumn start (September)

Semester 1 (30 credits)Semester 2 (30 credits)Semester 3 (30 credits)Semester 4 (30 credits)
  • Mathematical principles of AI (5)
  • Foundation of AI (5)
  • AI technologies (5)
  • AI in practice (5)
  • Elective – choose from sixteen (5)
  • Project laboratory 1 (5)
  • Advanced probability theory (5)
  • Machine learning (5)
  • Security in machine learning (5)
  • AI and ethics (3)
  • Elective – choose from sixteen (5)
  • Project laboratory 2 (5)
  • Free elective (2)
  • Deep learning (5)
  • AI and law (3)
  • Elective – choose from sixteen (5)
  • Elective – choose from sixteen (5)
  • Thesis project, part 1 (10)
  • Free elective (2)
  • AI-based business models (3)
  • Elective – choose from sixteen (5)
  • Thesis project, part 2 (20)
  • Free elective (2)

Spring start (February)

Semester 1 (30 credits)Semester 2 (30 credits)Semester 3 (30 credits)Semester 4 (30 credits)
  • Mathematical principles of AI (5)
  • Advanced probability theory (5)
  • Foundation of AI (5)
  • AI technologies (5)
  • Elective – choose from sixteen (5)
  • Project laboratory 1 (5)
  • Machine learning (5)
  • AI in practice (5)
  • Deep learning (5)
  • AI and ethics (3)
  • Elective – choose from sixteen (5)
  • Project laboratory 2 (5)
  • Free elective (2)
  • Security in machine learning (5)
  • AI and law (3)
  • Elective – choose from sixteen (5)
  • Elective – choose from sixteen (5)
  • Thesis project, part 1 (10)
  • Free elective (2)
  • AI-based business models (3)
  • Elective – choose from sixteen (5)
  • Thesis project, part 2 (20)
  • Free elective (2)

What each subject is about

Eleven compulsory subjects, plus the project laboratory and the thesis. Each one has a single main aim.

Mathematics

Mathematical principles of AI

5 credits · semester 1

Gives you the mathematical language the rest of the program is written in.

Linear algebra · Hilbert spaces and kernels (RKHS) · Optimization · Variational inference

Subject sheet

Advanced probability theory

5 credits · semester 2 (autumn start), 1 (spring start)

Teaches you to handle uncertainty with rigor: the probability behind learning from data.

Core AI

Foundation of AI

5 credits · semester 1

Maps the field: how AI systems infer, reason and represent knowledge, up to modern foundation models.

Inference · Causal reasoning · Transformers · Foundation models

Subject sheet

Machine learning

5 credits · semester 2

Shows how models are fitted to data, and how to say how sure you are about their answers.

Probabilistic machine learning · Bayesian methods and MCMC · Graphical models · Transformers

Subject sheet

AI technologies

5 credits · semester 1

Gives you the working toolkit: the software stack and how to run models in production.

The Python stack and frameworks · Retrieval-augmented generation (RAG) and agents · MLOps · FastAPI and Docker

Subject sheet

Deep learning

5 credits · semester 3 (autumn start), 2 (spring start)

Teaches you to build and train neural networks for images, text, sequences and graphs.

PyTorch and TensorFlow · CNNs, RNNs and transformers · Self-supervised learning · Deep reinforcement learning · Graph neural networks

Subject sheet

Security in machine learning

5 credits · semester 2 (autumn start), 3 (spring start)

Explains how machine learning systems get attacked and how to defend them.

Adversarial attacks · Data poisoning · Privacy

Subject sheet

AI in practice

5 credits · semester 1 (autumn start), 2 (spring start)

Uses existing models on real tasks, so you can build something that works.

Prompt engineering · Retrieval-augmented generation · Object detection with YOLO · Agents

Subject sheet

AI in society

AI-based business models

3 credits · semester 4

Shows how AI creates value inside companies and what makes an AI business work.

Subject sheet

AI and ethics

3 credits · semester 2

Trains you to think about fairness, responsibility and the effects on society of the systems you build.

Subject sheet

AI and law

3 credits · semester 3

Explains the legal frame around AI, including the EU AI Act.

Subject sheet

Project

Project laboratory 1 and 2

5 + 5 credits · semesters 1 and 2

Your first project of your own, with a supervisor. You must finish both parts before you start the thesis.

Thesis project

10 + 20 credits · semesters 3 and 4

Your own research or engineering project over two semesters. It ends with a thesis defense.

Professional internship

0 credits · recommended in semester 3

Six weeks in a company or lab. It has no credits, but you need it for the degree. Recommended in semester 3.

Most subject sheets on Eduinfo are written in Hungarian at the moment. The topic lists here are summaries.

Sixteen electives, choose five

You complete 25 credits of electives: five subjects at 5 credits each. They come from several departments of the faculty and from the Faculty of Mechanical Engineering. We group them by topic to help you browse.

Software and systems

AI-assisted software development
Using AI across the software lifecycle.
Development of AI-based software and services
Building AI services end to end: MLOps, mobile and cloud.
High performance embedded computing
Running demanding computation on embedded hardware.

Robots and vision

Intelligent robots
Kinematics, SLAM and motion planning.
Deep learning in visual computing
Neural networks for images and video.
Computer vision systems
How machines see: methods and complete vision systems.
AI-based control and multiagent robot coordination
Learning-based control and robots that work together.
Hardware-in-the-loop AI image processing
Hardware and software systems for AI-based image processing.

Data and decisions

Causal analysis and decision support
Do-calculus and causal discovery for better decisions.
Trustworthy AI and data analysis
Data quality and explainable AI (XAI).
Data science on structured and textual data
Analyzing tables and text.
AI-based optimization and anomaly detection
Optimization methods, and finding what does not fit.

Language, learning and people

Integrated large language models
RAG, MCP and agents built around LLMs.
Advanced machine learning
MLOps, time series, parameter-efficient fine-tuning, diffusion and federated learning.
UX&AI
Designing human-facing AI products.

Industry

AI in manufacturing
AI for production and manufacturing technology.

From first project to thesis

The last part of the program is yours. You work on a real problem with a supervisor and show what you can do.

  1. Project laboratory

    Two 5-credit parts. Your first project of your own, with a supervisor. You must finish both before the thesis.

  2. Competence check

    A check at the end of semester 2. Only students who finished both project laboratory parts may start the thesis.

  3. Internship and exchange

    A six-week internship, recommended in semester 3. Semester 3 is also the window for a semester abroad.

  4. Thesis

    Ten credits, then twenty, over two semesters. It ends with the defense, which is the final exam of the program.

Who this program is for

You need a solid computing background. The program starts at master's level, so you should be comfortable with programming and mathematics.

Accepted without conditions

  • BSc in computer engineering (mérnökinformatikus)
  • BSc in software engineering and computer science (programtervező informatikus)
  • BSc in business informatics (gazdaságinformatikus)

Accepted in part

  • BSc in engineering informatics (üzemmérnök-informatikus) and in cybersecurity engineering
  • Other degrees, decided by the faculty's credit-transfer committee

The credit rule

You need at least 50 credits from your earlier studies in the relevant fields, and at least 30 of them when you start. For degrees that are accepted in part, the 50 credits are 20 in mathematics and computer science and 30 in informatics. If up to 20 credits are missing, you make them up in the first two semesters, next to the master's subjects.

How admission is scored

Admission is scored out of 100 points: up to 45 for your earlier studies, up to 45 for a written exam, and up to 10 for other relevant achievements. The exam covers algorithms (one third) and artificial intelligence (two thirds).

  • Earlier studies, 45 points
  • Written exam, 45 points
  • Other achievements, 10 points

How to apply

Applicants from Hungary use the national admission system. International applicants apply through the BME Xplore portal.

Spring 2027 intake (February)

15 November 2026, midnight

This is the deadline for both groups below.

If you live in Hungary

  • Apply in the E-felvételi system on felvi.hu. You must finish and certify your application by 15 November 2026, midnight.
  • Documents you earn later can be uploaded until 7 January 2027.
  • Admission score thresholds are expected on 21 January 2027.
Go to felvi.hu

If you apply from abroad

Fee-paying international students apply from 15 October to 15 November 2026 on the BME Xplore portal.

Go to xplore.bme.hu

Documents for an MSc application

  • CV and motivation letter
  • Copy of your passport
  • BSc certificate and transcripts
  • Two recommendation letters
  • Proof of English at B2 level

Accepted proof of English (B2)

IELTS5.5
TOEFL iBT72
Cambridge B2160
PTE50
Duolingo100
OtherA Medium of Instruction certificate from your previous university

Scholarship applicants: the Stipendium Hungaricum call usually closes in mid-January.

BME open day: 27 November 2026. Register for the open day