Queen's School of Computing
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Practical applications of AI have exploded in recent years with a resurgence of research in machine learning and, especially, in deep learning. Real-world applications include medical diagnosis, text and voice analysis for big data applications, computer vision, and self-driving cars.

There is a growing demand for graduates with an AI background from the leading technology firms, healthcare companies, automobile manufacturers, and research labs. The creation of a "Field of Study in Artificial Intelligence" will help to distinguish Computing graduates and increase their competitiveness for these highly skilled positions. The School of Computing has substantial expertise in this Field, with core faculty members in AI covering diverse disciplines.

  • Dorothea Blostein: biomechanics; adaptive tensegrity
  • Steven Ding: data mining; machine learning; security
  • Qingling Duan: machine learning; biomedical computing; bioinformatics
  • Randy Ellis: medical data analysis
  • Sidney Givigi: machine learning applied to autonomous vehicles; especially reinforcement learning and deep learning
  • Ting Hu: evolutionary computing; machine learning; complex networks; bioinformatics
  • Parvin Mousavi: machine learning in computer assisted diagnosis and interventions; image-guided interventions; ultrasound imaging; medical image computing; computational biology; bioinformatics; systems biology
  • Christian Muise: automated planning; model understanding, learning, and acquisition; goal-oriented dialogue systems
  • Amber Simpson: machine learning; medical image analysis; computer aided surgery
  • David Skillicorn: adversarial knowledge discovery
  • Sameh Sorour: cyber-physical and autonomous systems; intelligent vehicles and transportation systems; mobile edge learning; Internet of Things (IoT); edge computing and networking; network coding
  • Catherine Stinson: bias and explanation in machine learning; computational psychiatry; embodied intelligence; community-led design
  • Yuan Tian: deep learning; software engineering; machine learning, recommender systems
  • Farhana Zulkernine: big and streaming data management and analytics; deep learning and Decision Support Systems (DSS); cognitive computing; knowledge management systems; cloud and services computing

The requirements of the Field are that the student take three courses in AI (described below), complete an AI-related thesis, and complete the other requirements of the degree program. Graduates that complete the requirements will receive the "Field of Artificial Intelligence" designation on their transcript.

MSc Program

For the MSc (research-based only), the Field of Study in Artificial Intelligence will require that student:

  • take three courses from the AI courses listed further below, including at least one of CISC 856, CISC 867, CISC 874.
  • take two more courses as required in the MSc program; and
  • complete an AI-related thesis.

PhD Program

For the PhD, the Field of Study in Artificial Intelligence will require that student:

  • satisfy three tokens that show graduate-course-equivalent experience in three of the AI courses listed further below, including at least one of CISC 856, CISC 867, CISC 874.
  • satisfy seven more tokens as required in the PhD program; and
  • complete an AI-related thesis.

Courses

  • CISC 856*/3.0 Reinforcement Learning
    This course includes topics on formal and heuristic approaches to problem solving, planning, reinforcement learning, knowledge representation and reasoning, Markov decision processes, dynamic programming, temporal difference learning, Monte Carlo reinforcement learning methods, function approximation methods, integration of learning and planning.
    Prerequisite: CISC-352 or equivalent, programming expertise.
  • CISC 867*/3.0 Deep Learning
    Teaches algorithms and concepts about deep learning based on the biological neural network. Students will learn about deep belief network, restricted Boltzmann machine, Convolutional, Generative adversarial and Long Short Term Recursive NN and develop DNN using tools such as TensorFlow to perform feature extraction, image recognition and text processing.
    Prerequisite: COGS 400/ CISC 452/CMPE 452 or co-requisite CISC 874
  • CISC 855*/3.0 Nonlinear Optimization
    Methods for computational optimization, particularly examining nonlinear functions of vectors. Topics may include: unconstrained optimization; first-order methods; second-order methods; convex problems; equality constraints; inequality constraints; applications in machine learning.
  • CISC 859*/3.0 Pattern Recognition
    An introduction to statistical and structural pattern recognition. Feature extraction and the feature space. Bayes decision theory. Parametric classification. Clustering methods. Syntactic pattern description: string, tree and graph grammars; attributed grammars; stochastic grammars. Error correcting parsing; parsing of stochastic languages. Assignments include practical experience in application areas such as character recognition and document image analysis.
  • CISC 873*/3.0 Data Mining
    Study of the extraction of concepts from large high-dimensional datasets. Statistical foundations; techniques such as supervised neural networks, unsupervised neural networks, decision trees, association rules, Bayesian classifiers, inductive logic programming, genetic algorithms, singular value decomposition, hierarchical clustering.
  • CISC 874*/3.0 Neural and Cognitive Computing
    Theoretical foundation and practical applications of Artificial Neural Networks (ANN) and Cognitive Computing (CC) models. Paradigms of neural computing algorithms using attention and context embedding models, applications in cognitive modeling, artificial intelligence, and machine learning with multi-stream data processing techniques.
  • CISC 843*/3.0 Mining Software Repositories
    Mining Software Repositories, Applied Machine Learning in Software Engineering, Automated Software Engineering, Empirical Software Engineering, Software Engineering for Artificial Intelligence.
  • CISC 851*/3.0 Evolutionary Computing
    This course offers a hands-on introduction to evolutionary computing, a field that amounts to building, applying and studying algorithms based on the Darwinian principles of natural evolution. Evolutionary computing, as a population-based search technique inspired by evolution, is able to create novel solutions and is often regarded as a creative approach to AI. Students will learn various evolutionary computing techniques including genetic algorithms, evolution strategies, and genetic programming, and will study their applications to optimization and learning problems.

Vector Scholarships in Artificial Intelligence (VSAI)

The Vector Scholarship in Artificial Intelligence is valued at $17,500 for one year of full-time study at an Ontario University. These merit-based entrance awards recognize exceptional candidates pursuing a master’s program, such as the Artificial Intelligence Field of Study, that is recognized by the Vector Institute or who are following an individualized study path in a range of disciplines that is demonstrably AI-focused. Both domestic and international students with first class standing (minimum A- in their last two years of full-time study) are eligible for consideration. Nominations for the 2020/21 academic year open on January 4, 2021.

Students persuing the AI field of study are encouraged to apply for the scholarship through Debby Robertson. Applications received by February 24 will be reviewed and given feedback before the final March 10th deadline.

  • February 24, 2021 - Deadline to get feedback (First come first serve basis)
  • March 10, 2021 - Final Deadline

Scholarship applications must be submitted through the program(s) the prospective student has applied to and include the following components:

  • copies of all official transcripts;
  • two references using the Referee Form (at least one of two references must be academic). Note that the referee forms are not the same forms submitted when you applied to your program. Forms should be submitted by the referee and not the applicant.
  • an up-to-date one to two-page CV; and
  • a 250-word statement outlining your reason for pursuing a master’s in AI, relevant AI-related experience, and career aspirations.
  • If not enrolled in a Vector-recognized program, an approved study plan including a course list and description of thesis/capstone project.
  • a self-identification questionnaire
Learn more about the scholarship and eligibility requirements.