Master of Science in Natural Language Processing - Artificial Intelligence
Abu Dhabi, United Arab Emirates
DURATION
2 Years
LANGUAGES
English
PACE
Full time
APPLICATION DEADLINE
31 Aug 2025*
EARLIEST START DATE
Aug 2025
TUITION FEES
Request tuition fees
STUDY FORMAT
On-Campus
* deadline for international students/deadline for UAE nationals: May 30th, 2025 | Application start date: Oct 1st, 2024
* *No tuition fee, free accommodation + monthly stipend of 2100USD+
Introduction
Natural language processing (NLP) focuses on system development that allows computers to communicate with people using everyday language. Natural language generation systems convert information from the computer database into readable or audible human language and vice versa. Such systems also enable sophisticated tasks such as inter-language translation, semantic understanding, text summarization and holding a dialog. The key applications of NLP algorithms include interactive voice response applications, automated translators, digital personal assistants (e.g., Siri, Cortana, Alexa), chatbots, and smart word processors.
Alumni Statistics
Admissions
Curriculum
The minimum degree requirements for the Master of Science in Natural Language Processing is 36 credits, distributed as follows:
| Core courses | Number of courses | Credit hours |
| Core | 3 | 12 |
| Electives | 3 | 12 |
| Research thesis | 1 | 12 |
| Internship | At least one internship of up to six weeks must be satisfactorily completed as a graduation requirement | 0 |
Core courses
The Master of Science in Natural Language Processing is primarily a research-based degree. The purpose of coursework is to equip students with the right skill set, so they can successfully accomplish their research project (thesis). Students are required to take AI701, MTH701 and NLP701 as mandatory courses. They can select three electives.
| Code | Course Title | Credit Hours |
| AI701 | Foundations of Artificial Intelligence | 4 |
| MTH701 | Mathematical Foundations of Artificial Intelligence | 4 |
| NLP701 | Natural Language Processing | 4 |
| NLP702 | Advanced Natural Language Processing | 4 |
| NLP703 | Speech Processing | 4 |
Elective courses
Students will select a minimum of three elective courses, with a total of 12 (or more) credit hours. Two must be selected from List A and one must be selected from List A or B based on interest, proposed research thesis, and career aspirations, in consultation with their supervisory panel. The elective courses available for the Master of Science in Natural Language Processing are listed in the tables below:
List A
| Code | Course Title | Credit Hours |
| NLP702 | Advanced Natural Language Processing | 4 |
| NLP703 | Speech Processing | 4 |
| NLP704 | Deep Learning for Language Processing | 4 |
| NLP705 | Topics in Advanced Natural Language Processing | 4 |
| NLP706 | Advanced Speech Processing | 4 |
List B
| Code | Course Title | Credit Hours |
| AI702 | Deep Learning | 4 |
| CV701 | Human and Computer Vision | 4 |
| CV702 | Geometry for Computer Vision | 4 |
| CV703 | Visual Object Recognition and Detection | 4 |
| CV707 | Digital Twins | 4 |
| DS701 | Data Mining | 4 |
| DS702 | Big Data Processing | 4 |
| HC701 | Medical Imaging: Physics and Analysis | 4 |
| ML701 | Machine Learning | 4 |
| ML702 | Advancing Machine Learning | 4 |
| ML703 | Probabilistic and Statistical Inference | 4 |
| ML707 | Smart City Services and Applications | 4 |
| ML708 | Trustworthy Artificial Intelligence | 4 |
| MTH702 | Optimization | 4 |
Research thesis
Master’s thesis research exposes students to an unsolved research problem, where they are required to propose new solutions and contribute towards the body of knowledge. Students pursue an independent research study, under the guidance of a supervisory panel, for a period of one year.
| Code | Course Title | Credit Hours |
| NLP699 | Natural Language Processing Master’s Research Thesis | 12 |
Gallery
Ideal Students
STEM major students with GPA above 3.2/4.0
Rankings
CS Rankings in a Glance
- 18th in the field of AI in CS Rankings globally
- 28th in the field of ML in CS Rankings globally
- 16th in the field of CV in CS Rankings globally
- 19th in the field of NLP in CS Rankings globally
Program Outcome
Upon completion of the program requirements, the graduate will be able to:
- Demonstrate highly specialized understanding of the computational techniques for analyzing and modeling textual and speech data with applications to real-world scenarios
- Have a deep understanding of the syntactic and semantic structures in speech and textual data (e.g., the predicate-argument structure)
- Obtain advanced capabilities to implement the cutting-edge NLP algorithms, and benchmark the achieved results
- Have the capability to formulate own research questions, analyze the existing body of knowledge, propose, and develop solutions to new problems
- Obtain expertise in using and deploying NLP related programming tools for a variety of NLP problems
- Work independently as well as part of a team, in a collegial manner, on NLP related projects
- Effectively communicate experimental results and research findings orally and in writing, and critique existing body of work
Program Tuition Fee
Career Opportunities
AI is permeating every industry. At recent employer engagement events at MBZUAI, there has been representation from multiples sectors including (but not limited to):
- Aviation, consultancy, education, energy, finance, government entities, healthcare, media, oil and gas, security and defense, research institutes, retail, telecommunications, transportation and logistics, and startups.
Recent job opportunities advertised via the MBZUAI Student Careers Portal include (but not limited to):
- AI solution architect, AI solution engineer, algorithmic engineer, data analyst, data engineer, data scientist, data strategy consultant, full stack software engineer, full stack web developer, predictive analytics researcher, and senior data scientist – consultant.
Other career opportunities could include (but not limited to):
- Applied scientist, analytics engineer, augmented/virtual reality, autonomous cars, biometrics and forensics, chief data officer, data platform leadership, data journalist, data and AI technical sales specialist, growth analytics / engineers, manager: AI and cloud services planning, machine learning engineers, product manager: AI and data analytics, product data scientist, product analyst, remote sensing, research assistants, security and surveillance, senior software engineer, and VP data.