Doctor of Philosophy in Machine Learning - Artificial Intelligence

The scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. These algorithms are based on mathematical models learned automatically from data, thus allowing machines to intelligently interpret and analyze input data to derive useful knowledge and arrive at important conclusions. Machine learning is heavily used for enterprise applications (e.g., business intelligence and analytics), effective web search, robotics, smart cities, and understanding of the human genome.

Alumni Statistics

The minimum degree requirements for the Doctor of Philosophy in Machine Learning is 60 credits, distributed as follows:

Core courses

The Doctor of Philosophy in Machine Learning is primarily a research-based degree. The purpose of coursework is to equip students with the right skillset, so they can successfully accomplish their research project (thesis). Students are required to take AI701, MTH701 and ML701 as mandatory courses. They can select either ML702 or ML703 along with two electives.

Elective courses

Students will select a minimum of two elective courses, with a total of eight (or more) credit hours. One 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. l. The elective courses available for the Doctor of Philosophy in Machine Learning are listed in the tables below:

List A

List B

Research thesis

The Ph.D. thesis exposes students to cutting-edge and unsolved research problems in the field of machine learning, where they are required to propose new solutions and significantly contribute towards the body of knowledge. Students pursue an independent research study, under the guidance of a supervisory panel, for a period of three to four years.

CS Rankings in a Glance

• 20th in the field of AI in CS Rankings globally
• 28th in the field of ML in CS Rankings globally
• 27th in the field of CV in CS Rankings globally
• 24th in the field of NLP in CS Rankings globally

Upon completion of the program requirements, the graduate will be able to:

1. Obtain rigorous mathematical background and advanced reasoning capabilities to express comprehensive and deep understanding of the pipelines at the frontier of machine learning: data, models, algorithmic principles, and empirics.
2. Master a range of skills and techniques in data-preprocessing, exploration, and visualization of data-statistics as well as complex algorithmic outcomes
3. Have a critical awareness of the capabilities and limitations of the different forms of learning algorithms and the ability to critically analyze, evaluate, and improve the performance of the learning algorithms
4. Grow expert problem-solving skills through independently applying the principles and methods learned in the program to various complex real-world problems
5. Develop a deep understanding of statistical properties and performance guarantees including convergence rates (in theory and practice) for different learning algorithms
6. Become an expert in using and deploying machine learning-relevant programming tools for a variety of machine learning problems
7. Grow proficiency in identifying the limitations of existing machine learning algorithms and the ability to conceptualize, design, and implement an innovative solution for a variety of highly complex problems to advance the state-of-the-art in machine learning
8. Able to initiate, manage, and complete research manuscripts that demonstrates expert self-evaluation and advanced skills in communicating highly complex ideas related to machine learning
9. Obtain highly sophisticated skills in initiating, managing, and completing multiple project reports and critiques on a variety of machine learning methods, that demonstrates expert understanding, self-evaluation, and advanced skills in communicating highly complex ideas

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.