International applicants must obtain a minimum English proficiency test score depending on program. Click here for more information.

Post-Baccalaureate Diploma in Artificial Intelligence (AI) and Machine Learning Applications

AI IS CHANGING THE WORLD.

BE PART OF THE CHANGE.

Become part of a changing world and discover Nipissing University’s Post-Baccalaureate Diploma (PBD) in Artificial Intelligence and Machine Learning Application. This two-year program is designed for international students who already have an undergraduate degree in computer science or a related discipline and who are ready to explore advanced, state-of-the-art knowledge of new computational techniques and paradigms, including machine learning, artificial intelligence, advance visualization and graphics, and data science.

This program will enhance your knowledge of emerging and increasingly important interdisciplinary professions in areas such as data analytics, “Big Data”, high performance computing and parallel computing, game artificial intelligence and other technologies, geomatics information systems, and computational tools and techniques in the humanities.

North Bay

Ontario, Canada

5500

Students

70

Programs

$4.9 million

In scholarships, bursaries and awards

WHY CHOOSE NIPISSING UNIVERSITY?

At Nipissing University, we believe our small size is one of your biggest advantages. Our focus on student success is consistently recognized with top rankings in Canada in the areas of student support, student experience, faculty, and residences.

Our personalized and innovative approaches to teaching create a high-quality learning environment where you can engage in lively debate and discussion with your peers and faculty.

Learn more about Nipissing University Learn more about North Bay

YOUR CAREER

Graduates of the Nipissing Post-Baccalaureate Diploma (PBD) in Artificial Intelligence and Machine Learning Applications can pursue a wide variety of careers including roles in software development, data science, scientific computing, scientific software engineering, game development, and machine learning engineering.

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Admission Requirements

Students must have an undergraduate degree with a GPA of 70% or higher and meet English language proficiency requirements.

Explore your diploma course options

Choose your courses based on what interests you the most!

Some courses are required for graduation and have their own prerequisites.

Click here to view the full list of courses and program requirements for this diploma.

Below are some courses we think you'll like!

Artificial Intelligence

COSC 3007

Delve into the history and applications of artificial intelligence. Topics include: state spaces and search strategies, machine learning, genetic algorithms, artificial neural networks; capabilities and limitations of artificial intelligence; applications in expert systems, natural languages, robotics, speech, and vision; interaction with an existing expert system; construction of a small expert system; using artificial neural networks to perform image recognition and system control.

Digital Ethics

PHIL 2816

Examine ethical issues that have emerged in relation to digital technologies, such as: the meaning and value of privacy; the right to be forgotten; the power of search engines; the use of Big Data and Big Data analytics; equality and the “digital divide”; censorship and free speech online; the ethics of the online self, including questions of reification, catfishing; polarization and the internet; artificial intelligence; and the ethics of hacking and hacktivism.

Programming Paradigms

COSC 3306

This course introduces the alternative programming paradigms and languages. Topics include: overview of functional, logic, and object-oriented paradigms and languages, designing programs with these paradigms, advantages and disadvantages of alternative programming paradigms vs. procedural programming. Applications in AI, database and software design are introduced.

Human-Computer Interaction

COSC 3106

Explore the interactions between people and computers; learn about tradeoffs in human-computer interaction (HCI) design and evaluate alternative solutions. Topics include usability and affordances, direct manipulation, systematic design methods, user conceptual models and interface metaphors, human cognitive models, physical ergonomics, information and interactivity structures, design tools and environments, user-centered design, and universal design.

Advanced Game Design and Development

COSC 3406

This course presents a rigorous approach to the design and development of computer games, emphasizing the computational and programming tasks involved. Students will learn the basics of physics simulation, graphics, audio, 2D/3D art, and software engineering, as applied to game development. Topics such as the game engine, sound, rendering, modeling, and user interfaces will also be explored. Knowledge of these topics will be applied to the development of game-oriented projects. This is primarily a hands-on course where real-world skills, including design, teamwork, management, documentation, and effective communication are critical.

Machine Learning

COSC 3006

Learn about the main machine learning technologies and tools used in data science and data analytics, including clustering, decision trees, recurrent and convolutional neural networks, support vector machines, Bayesian learning, reinforcement learning. Students explore and apply methods from computational intelligence through a variety of applications and solutions to problems in data science and data analytics.

Data Analytics

DATA 4006

Engage with the principles, concepts, and techniques of data analytics and its applications. Students apply their knowledge through the presentation of state-of-the-art technologies and data science-related software. Topics include: Data analytics applications in the R and/or Python programming languages. Other topics include: statistics for “Big Data”; advanced statistical analysis of large volumes of high-frequency, heterogeneous data; data transformations; visual analytics; database issues and NoSQL; research into data analytics and methodologies.

Computer Graphics

COSC 3207

This course introduces the principles and methods of computer graphics and their applications. Topics include: PC video cards and storage; display devices; representing objects; raster algorithms for lines, circles and region filling; 2-D and 3-D graphics software; object transformations, fractal construction and animation software.