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PhD in

Machine Learning

Overview

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.

  • icon Full time Mode
  • icon 60 Credits
  • icon On Campus Location

The Machine Learning (ML) Department at MBZUAI is dedicated to imparting a world-class education in ML to our students. From foundational principles to advanced applications, our research-intensive education model will provide our students theoretical concepts to test under supervision from senior AI researchers in the field as they tackle real-world problems and produce meaningful results.

Kun Zhang

Acting Chair of Machine Learning, Professor of Machine Learning, and Director of Center for Integrative Artificial Intelligence (CIAI)

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Meet the Faculty

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Eric Xing

President and University Professor

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Kun Zhang

Acting Chair of Machine Learning, Professor of Machine Learning, and Director of Center for Integrative Artificial Intelligence (CIAI)

BIO
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Martin Takáč

Deputy Department Chair of Machine Learning, and Associate Professor of Machine Learning

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Mohsen Guizani

Professor of Machine Learning

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Le Song

Professor of Machine Learning

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Bin Gu

Assistant Professor of Machine Learning

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Qirong Ho

Assistant Professor of Machine Learning

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Samuel Horváth

Assistant Professor of Machine Learning

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Zhiqiang Xu

Assistant Professor of Machine Learning

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Eric Moulines

Adjunct Professor of Machine Learning

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Pengtao Xie

Adjunct Assistant Professor of Machine Learning

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Chih-Jen Lin

Affiliated Professor of Machine Learning

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Huseyin Ucar

Visiting Assistant Professor of Machine Learning

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Jin Tian

Professor of Machine Learning

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Maxim Panov

Assistant Professor of Machine Learning

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Mingming Gong

Affiliated Associate Professor of Machine Learning

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Nils Lukas

Assistant Professor of Machine Learning

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Praneeth Vepakomma

Assistant Professor of Machine Learning

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Michalis Vazirgiannis

Adjunct Professor of Machine Learning

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Fakhreddine (Fakhri) Karray

Professor of Machine Learning

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Gus Xia

Assistant Professor of Machine Learning

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Tongliang Liu

Affiliated Associate Professor of Machine Learning

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Salem Lahlou

Assistant Professor of Machine Learning

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Yuanzhi Li

Affiliated Assistant Professor of Machine Learning

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Zhiqiang Shen

Assistant Professor of Machine Learning

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A typical study plan is as follows:


SEMESTER 1

ML801 - Foundations and Advanced Topics in Machine Learning (4 CR)
+ 2 Electives (8 CR)

SEMESTER 2

ML814 - Selected Topics in Machine Learning (4 CR)
+ 2 Electives (8 CR)

SUMMER

INT899 INT899 Internship (up to four months)

SEMESTER 3

ML899 Ph.D. Research Thesis
RES899 Research Training

SEMESTER 4

ML899 Ph.D. Research Thesis

SEMESTER 5

ML899 Ph.D. Research Thesis

SEMESTER 6

ML899 Ph.D. Research Thesis

SEMESTER 7

ML899 Ph.D. Research Thesis

SEMESTER 8

ML899 Ph.D. Research Thesis

Disclaimer: Subject to change.


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