Vector Institute Collaborative Specialization in AI
Artificial intelligence is a well-established branch of computer science concerned with methods to make computers, or machines in general, intelligent. More specifically an artificially intelligent system is one that can learn on its own from experience and derive implicit knowledge. Examples of commonly used applications that use AI include spam filters, Google’s gmail categorization into primary, social and promotion inboxes, product recommendations, and personal assistants (e.g., Alexa, Siri). On the horizon we see the promise of AI in early diagnosis of cancer, autonomous vehicles, Industry 4.0 and neuroscience. The techniques used in AI are as diverse as the problems tackled, ranging from classical logic to statistical approaches based on the model of the brain (deep learning).
The combination of large amounts of data (“big data”) from various sources (e.g., social media, sensor data, public data, medical data, business transactions, social media), advances in computing power, recent AI research and recent successes in using AI for real-world applications has led to a growing demand for AI knowledge and skills.
To support the growing demand, the Department of Computer Science in conjunction with the Department of Electrical and Computer Engineering (CSAI) offers the Vector Institute Accredited Graduate Collaborative Specialization in Artificial Intelligence (CSAI). This program enhances the education and research of a graduate student in one of the participating programs by adding a module to their program of study which will provide training in Artificial Intelligence (AI) with a strong focus on artificial intelligence methodologies and enabling technologies with applications in their core research area. The student receives a degree from his or her home department program along with the annotation Artificial Intelligence.
The goals of the programs are: i) to provide an excellent foundation in the techniques and methodologies used in AI; ii) to encourage interdisciplinary actions in AI e.g., by fostering collaborative research; iii) to train students to recognize the broader ethical and social implications of AI.
- Students will be expected to apply AI methodologies to real problems that do not easily fit within a course structure.
- Students will be exposed to the synergy between AI methodologies and the technologies that make AI usable.
- Students will be exposed to ethical & legal challenges/limitations/ of AI methodologies.
- Students will be involved in interdisciplinary research projects.
- CSAI students will be exposed to Western collaboration space where industry and government partners, technical experts, and social science and humanities scholars come together to pose questions and share expertise regarding AI/ML technologies
Requirements
The courses to be taken have been organized as machine learning, advanced artificial intelligence which includes deep learning and the enabling technologies that allow for AI analysis. Students are required to take the following courses:
- The PHIL 9232B seminar course
- DS 9000: Intro to Machine Learning
- CS 9542: Artificial Intelligence II
- One of these courses:
- CS 9635: Distributed and Parallel Systems
- CS 9647: Unstructured Data
- For MSc thesis students the successful completion of thesis work in AI
- For MSc coursework students the successful completion of a directed study in AI
One of the co-Directors of CSAI can grant alternative choices.
Please note that the above courses can be counted to the MSc thesis and the MSc coursework options e.g., a student does not have to take these in addition to the course requirements for the MSc thesis and MSc coursework options.
Eligibility
Only full-time MSc students (thesis and coursework) in Computer Science are eligible to be considered for this program. Acceptance to the program to be approved by CS Co-Director.
