Artificial Intelligence - Architecture, Design, and Implementation

Program: AIDI
Credential: Ontario College Graduate Certificate
Delivery: Full-time + Part-time
Length: 2 Semesters
Duration: 1 Year
Effective: Fall 2024, Winter 2025, Summer 2025
Location: Barrie


The Artificial Intelligence (AI) computing paradigm radically changes the functionality and capabilities of computer systems. This greatly increases the possibilities of what businesses can do with this exciting new technology and is causing disruption across all industry sectors. Artificial Intelligence systems can think, learn and take self-directed action in order to maximize the chance of successfully achieving a goal without having to be explicitly programmed or human intervention.

This program provides students with the necessary background to become Artificial Intelligence (AI) system designers, programmers, implementers, or machine learning analysts. With a strong focus on applied skills, students learn how to design and implement supervised, unsupervised and reinforcement learning solutions for a variety of situations and solve AI challenges for a diverse set of industries. 

Advanced study in AI infrastructure, architecture, machine learning frameworks, reinforcement learning, neural networks, vision system, conversational AI and deep learning help students understand how to select, configure and apply the right technology tools to build the correct AI solution to solve a given challenge.

Career Opportunities

Graduates from this program are prepared to fulfill a wide-range of entry-level roles related to Artificial Intelligence which may include Artificial Intelligence (AI) system designers, programmers, implementers, or machine learning technologists. Graduates could find themselves working independently or as a member of a team to analyze, design, enhance, and maintain AI systems. 

Program Learning Outcomes

The graduate has reliably demonstrated the ability to:

  1. collect, manipulate and mine data sets to meet organizational need;
  2. recommend different systems architectures and data storage technologies to support data analytics; 
  3. design and apply data models that meet the needs of a specific operational/business process; 
  4. develop software applications to manipulate data sets, correlate information and produce reports;
  5. design and present data visualizations to communicate information to business stakeholders:
  6. apply data analytics, business intelligence tools and research to support evidence-based decision making; 
  7. identify and assess data analytics business strategies and workflows to respond to new opportunities or provide project solutions;
  8. implement artificial intelligence (AI) solutions in compliance with corporate policies, ethical standards, and industry regulations;
  9. develop artificial intelligence (AI) models and agents that use enterprise data to identify patterns, provide insights, recommend actions or perform tasks autonomously on behalf of stakeholders;
  10. analyze, design, and implement artificial intelligence (AI) systems through the application of systematic approaches and methodologies to meet organizational needs.

Program Progression 

The following reflects the planned progression for full-time offerings of the program.

Fall - Barrie

  • Sem 1: Fall 2024
  • Sem 2: Winter 2025

Winter - Barrie

  • Sem 1: Winter 2025
  • Sem 2: Summer 2025

Summer - Barrie

  • Sem 1: Summer 2025
  • Sem 2: Fall 2025

Admission Requirements

Ontario College Diploma, Ontario College Advanced Diploma, degree or equivalent with a focus in computer studies, technology, engineering, analytics, mathematics or statistics, or equivalent work experience, is required. 

Selection Process

Applicants may be asked to submit a current resume and a letter of interest to the Program Coordinator in order to assess their prior academic and experiential learning.

Additional Information

To be successful in this program, students are required to have a Windows-based personal notebook computer prior to the start of the program that meets or exceeds the following hardware specifications:

  • Intel I7, AMD A10 processor or better
  • 16 GB of RAM 
  • 1 TB hard drive 
  • Ethernet Network Card (Can be USB)
  • Wireless Network Card 
  • 2 USB 3.0 ports

Graduation Requirements

14 Program Courses

Graduation Eligibility

To graduate from this program, a student must attain a minimum of 60% or a letter grade of P (Pass) or S (Satisfactory) in each course in each semester. The passing weighted average for promotion through each semester and to graduate is 60%.

Program Tracking

The following reflects the planned progression for full-time offerings of the program.

Plan of Study Grid
Semester 1Hours
Program Courses  
AIDI 1000 Artificial Intelligence Algorithms and Mathematics  56
AIDI 1001 Conversational Artificial Intelligence 42
AIDI 1002 Machine Learning Programming 42
AIDI 1003 Machine Learning Frameworks 42
AIDI 1004 Issues and Challenges in Artificial Intelligence 42
AIDI 1005 Artificial Intelligence for Business Decision Making 42
BDAT 1000 Data Manipulation Techniques 42
Semester 2
Program Courses  
AIDI 1006 Artificial Intelligence Infrastructure and Architecture  42
AIDI 1007 Vision Systems 42
AIDI 1008 Reinforcement Learning Programming 42
AIDI 1009 Neural Networks 42
AIDI 1010 Emerging Artificial Intelligence Technologies 42
AIDI 1011 Artificial Intelligence Project 56
AIDI 1012 Artificial Intelligence Robotics and Automation 42
 Total Hours616

Graduation Window

Students unable to adhere to the program duration of one year (as stated above) may take a maximum of two years to complete their credential. After this time, students must be re-admitted into the program, and follow the curriculum in place at the time of re-admission.

Disclaimer: The information in this document is correct at the time of publication. Academic content of programs and courses is revised on an ongoing basis to ensure relevance to changing educational objectives and employment market needs. 

Program outlines may be subject to change in response to emerging situations, in order to facilitate student achievement of the learning outcomes required for graduation. Components such as courses, progression, coop work terms, placements, internships and other requirements may be delivered differently than published.

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