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:
- Exhibit highly specialized understanding of the modern machine learning pipeline: data, models, algorithmic principles, and empirics.
- Achieve advanced skills in data-preprocessing and using various exploration and visualization tools.
- Demonstrate critical awareness of the capabilities and limitations of the different forms of learning algorithms.
- Obtain advanced capabilities to critically analyze, evaluate, and continuously improve the performance of learning algorithms.
- Acquire advanced abilities to analyze computational and statistical properties of advanced learning algorithms and their performance.
- Gain expertise in using and deploying machine learning-relevant programming tools for a variety of complex machine learning problems.
- Develop advanced problem-solving skills through independently applying machine learning methods to multiple complex problems, and demonstrate expertise in dealing with ambiguity in a problem statement.
- Apply sophisticated skills in initiating, managing, and completing multiple project reports and critiques on variety of machine learning methods, that demonstrate expert understanding, self-evaluation, and advanced skills in communicating highly complex ideas
The minimum degree requirements for the Master’s of Science in Machine Learning program are 35 Credits, distributed as follows:
MSc 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 six in the list provided below:
Research Communication and Dissemination*
Advanced Machine Learning
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 Master’s of Machine Learning are listed in the below table:
Mathematical Foundations for Artificial Intelligence
Big Data Processing
Medical Imaging: Physics and Analysis
Human and Computer Vision
Geometry for Computer Vision
Visual Object Recognition and Detection
Natural Language Processing
Advanced Natural Language Processing
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 1 year.
Master’s Research Thesis