Artificial Intelligence - Architecture, Design, and Implementation

Description

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 and machine learning;
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 support decision-making for interested parties in AI-driven projects;
6. synthesize data analysis insights and business intelligence to support evidence-based decision making;
7. assess data analytics business strategies and workflows to respond to new opportunities or provide project solutions;
8. comply with corporate policies, ethical standards, and industry regulations during the design and deployment of AI solutions;
9. develop artificial intelligence (AI) models and agents to perform autonomous tasks on behalf of users;
10. design and deploy AI systems and machine learning models to solve organizational problems and enhance performance;
11. develop and apply strategies for professional development to enhance work performance and maintain currency with industry trends.

Program Progression 

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

Intake suspension Fall 2027

Fall Intake

• Sem 1: Fall 2027
• Sem 2: Winter 2028

Intake suspension Winter 2028

Winter Intake

• Sem 1: Winter 2028
• Sem 2: Summer 2028

Intake suspension Summer 2028

Summer Intake

• Sem 1: Summer 2028
• Sem 2: Fall 2028

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

Bring Your Own Device (BYOD) requirements

All Georgian College students are required to bring their own device (BYOD) that meets the technology requirements for their program. Having access to a personal laptop that meets your program’s technical requirements will ensure you’re prepared for in-class, online and hybrid learning activities. Students are responsible for the cost, software installation, maintenance and repair of their device.  

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 1		Hours
Program Courses
AIDI 1000	Artificial Intelligence Algorithms and Mathematics	56
AIDI 1001	Conversational Artificial Intelligence	42
AIDI 1002	Python Programming for Data Science	42
AIDI 1003	Machine Learning Frameworks and Model Foundations	42
AIDI 1004	Issues, Challenges, and Opportunities in Artificial Intelligence	42
AIDI 1014	Agentic and Generative Artificial Intelligence	42
BDAT 1010	Business Intelligence	42
	Hours	308
Semester 2
Program Courses
AIDI 1006	Artificial Intelligence Infrastructure and Architecture	42
AIDI 1007	AI-based Vision Systems	42
AIDI 1008	Reinforcement Learning Programming	42
AIDI 1009	Neural Networks, Optimization and Deployment	42
AIDI 1010	Emerging Artificial Intelligence Technologies	42
AIDI 1011	Artificial Intelligence Capstone Project	56
AIDI 1012	Artificial Intelligence Robotics and Automation	42
	Hours	308
	Total Hours	616

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.