Master’s of Science in Machine Learning

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

  1. Exhibit a highly specialized understanding of the modern machine learning pipeline: data, models, algorithmic principles, and empirics.
  2. Achieve advanced skills in data-preprocessing and using various exploration and visualization tools.
  3. Demonstrate a critical awareness of the capabilities and limitations of the different forms of learning algorithms.
  4. Obtain advanced capabilities to critically analyze, evaluate, and continuously improve the performance of learning algorithms.
  5. Acquire advanced abilities to analyze computational and statistical properties of advanced learning algorithms and their performance.
  6. Gain expertise in using and deploying machine learning-relevant programming tools for a variety of complex machine learning problems.
  7. 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.
  8. Apply sophisticated skills in initiating, managing, and completing multiple project reports and critiques on a 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:

  • Core Courses: 4 Courses (15 Credit Hours)
  • Elective Courses: 2 Courses (8 Credit Hours)
  • Research Thesis: 1 Course (12 Credit Hours)

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Core Courses

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:

Code Course Title Credit Hours
COM701 Research Communication and Dissemination 3
ML701 Machine Learning 4
ML702 Advanced Machine Learning 4
ML703 Probabilistic and Statistical Inference 4
MTH701 Mathematical Foundations for Artificial Intelligence 4
AI701 Artificial Intelligence 4
AI702 Deep Learning 4

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 Master’s of Machine Learning are listed in the below table:

Code Course Title Credit Hours
MTH702 Optimization 4
CS701 Advanced Programming 4
CS702 Data Structures and Algorithms 4
DS701 Data Mining 4
DS702 Big Data Processing 4
CV701 Human and Computer Vision 4
CV702 Geometry for Computer Vision 4
CV703 Visual Object Recognition and Detection 4
NLP701 Natural Language Processing 4
NLP702 Advanced Natural Language Processing 4
NLP703 Speech Processing 4
ML704 Machine Learning Paradigms 4
ML705 Topics in Advanced Machine Learning 4
ML706 Advanced Probabilistic and Statistical Inference 4
HC701 Medical Imaging: Physics and Analysis 4

Research Thesis

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.

Code Course Title Credit Hours
ML699 Master’s Research Thesis 12
Program taught in:
  • English (US)

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Last updated October 17, 2019
This course is Campus based
Start Date
Sep 2020
Duration
2 years
Price
- Covered by full scholarship.
Deadline
July 15, 2020
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By date
Start Date
Sep 2020
End Date
Application deadline
July 15, 2020

Sep 2020

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Application deadline
July 15, 2020
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