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.
Upon completion of the program requirements, the graduate will be able to:
- 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.
- Master a range of skills and techniques in data-preprocessing, exploration, and visualization of data-statistics as well as complex algorithmic outcomes.
- 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.
- Grow expert problem-solving skills through independently applying the principles and methods learned in the program to various complex real-world problems.
- Develop a deep understanding of statistical properties and performance guarantees, including convergence rates (in theory and practice) for different learning algorithms.
- Become an expert in using and deploying machine learning-relevant programming tools for a variety of machine learning problems.
- 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.
- 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.
- 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:
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:
Research Communication and Dissemination*
Advanced Machine Learning
Probabilistic and Statistical Inference
Machine Learning Paradigms
Topics in Advanced Machine Learning
Advanced Probabilistic and Statistical Inference
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:
Mathematical Foundations for Artificial Intelligence
Data Structures and Algorithms
Big Data Processing
Human and Computer Vision
Geometry for Computer Vision
Visual Object Recognition and Detection
Natural Language Processing
Advanced Natural Language Processing
Medical Imaging: Physics and Analysis
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.
PhD Research Thesis