MBZUAI
Mohamed Bin Zayed University of Artificial Intelligence Mohamed Bin Zayed University of Artificial Intelligence

Program details

Introduction

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.

Program
Competence

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.

The minimum degree requirements for the “PhD in Machine Learning” are 59 Credits, distributed as follows:

Core Courses
Number of Courses
Credit Hours
Core Courses
4
15 Credit Hours
Elective Courses
2
8 Credit Hours
Research Thesis
1
36 Credit Hours

Core Courses

PhD 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 COM701, as a mandatory course. They can select three core courses from a concentration pool of eight in the list provided below:

Code
Course Title
Credit Hours
COM701

Research Communication and Dissemination*

3 Credit Hours
ML701

Machine Learning

4 Credit Hours
ML702

Advanced Machine Learning

4 Credit Hours
ML703

Probabilistic and Statistical Inference

4 Credit Hours
ML704

Machine Learning Paradigms

4 Credit Hours
ML705

Topics in Advanced Machine Learning

4 Credit Hours
ML706

Advanced Probabilistic and Statistical Inference

4 Credit Hours

Elective Courses

Students will select a minimum of two elective courses, with a total of eight (or more) credit hours (CH) from a list of available elective courses based on interest, proposed research thesis, and career perspectives, in consultation with their supervisory panel. The elective courses available for the PhD in Machine Learning are listed in below table:

Code
Course Title
Credit Hours
MTH701

Mathematical Foundations for Artificial Intelligence

4 Credit Hours
MTH702

Optimization

4 Credit Hours
AI701

Artificial Intelligence

4 Credit Hours
AI702

Deep Learning

4 Credit Hours
DS701

Data Mining

4 Credit Hours
DS702

Big Data Processing

4 Credit Hours
CV701

Human and Computer Vision

4 Credit Hours
CV702

Geometry for Computer Vision

4 Credit Hours
CV703

Visual Object Recognition and Detection

4 Credit Hours
CV704

Advanced Computer Vision

4 Credit Hours
CV705

Advanced 3D Computer Vision

4 Credit Hours
CV706

Neural Networks for Object Recognition and Detection

4 Credit Hours
NLP701

Natural Language Processing

4 Credit Hours
NLP702

Advanced Natural Language Processing

4 Credit Hours
NLP703

Speech Processing

4 Credit Hours
NLP704

Deep Learning for Language Processing

4 Credit Hours
NLP705

Topics in Advanced Natural Language Processing

4 Credit Hours
NLP706

Advanced Speech Processing

4 Credit Hours

Research Thesis

PhD 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 3 - 4 years.

Code
Course Title
Credit Hours
ML799

PhD Research Thesis

36 Credit Hours