Applied Artificial Intelligence & Machine Learning
(Optional Co-op)

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Courses - May 2023

Level 1

Course details

Career Management
CDEV8130

Description:

This course focuses on career management skills needed to navigate the evolving workplace. Students will evaluate their skills, attitudes, and expectations within their chosen careers and explore emerging trends in the workplace. Students will refine their networking strategies and create marketing documents to position them for success. Mock interviews will provide the opportunity for practice, feedback, and reflection as students prepare for future interviews. Students will explore communication strategies that support workplace success and advancement. By the end of this course, students will have created a personalized career management plan.

  • Hours: 28
  • Credits: 2
  • Pre-Requisites:
  • CoRequisites:

Conestoga 101
CON0101

Description:

This self-directed course focuses on introducing new students to the supports, services, and opportunities available at Conestoga College. By the end of this course, students will understand the academic expectations of the Conestoga learning environment, as well as the supports available to ensure their academic success. Students will also be able to identify on-campus services that support their health and wellness, and explore ways to get actively involved in the Conestoga community through co-curricular learning opportunities.

  • Hours: 1
  • Credits: 0
  • Pre-Requisites:
  • CoRequisites:

Artificial Intelligence Algorithms and Mathematics 
CSCN8000

Description:

This course introduces students to the mathematical foundations behind Artificial Intelligence. In addition to mathematical principles, context is provided around the development and evolution of AI algorithms throughout history, including an overview of Game Theory.

  • Hours: 42
  • Credits: 3
  • Pre-Requisites:
  • CoRequisites:

Foundations of Machine Learning Frameworks 
CSCN8010

Description:

Many strategies and techniques are explored during this course that can be applied to machine learning workloads. Students will gain important background information and practical implementation experience on software design and development techniques and how they have evolved over the years.

  • Hours: 56
  • Credits: 4
  • Pre-Requisites:
  • CoRequisites:

Cyberlaw, Ethics and Privacy
INFO8620

Description:

An important component of application and organizational security is the understanding of the rights and obligations of both the individual and the organization with respect to privacy and intellectual property. In this course students will explore ethics and law, including both Canadian law and the law of other jurisdictions, for the ways ethics and law inform the best practices of security professionals and organizations. Students will learn to assess the implications of new application development as well as the choice of supporting technologies, such as cloud computing, with respect to both vulnerabilities and liabilities of an organization. Students in this course will discuss topics such as Big Data, organized hacking, government surveillance, and industrial espionage.

  • Hours: 42
  • Credits: 3
  • Pre-Requisites:
  • CoRequisites:

Machine Learning Programming
PROG8245

Description:

Students will enhance their existing programming skills through the introduction of programming platforms like Python to develop and resolve challenges in Machine Learning software development.

  • Hours: 56
  • Credits: 4
  • Pre-Requisites:
  • CoRequisites:

Data Analysis Mathematics, Algorithms and Modeling
PROG8430

Description:

The fundamentals of Big Data Analysis are rooted in traditional statistics and advanced statistical models. Students will begin this course by reviewing ways in which a common tool (like a spreadsheet) provides powerful statistical analysis tools. Then, students will implement some formulae and algorithms used for Big Data analysis in custom computer programs (using, for example, R) and specialized software packages like SPSS, MATLAB and Statistics. Finally, students will examine topics in predictive and prescriptive modeling.

  • Hours: 42
  • Credits: 3
  • Pre-Requisites:
  • CoRequisites:

Agile Software Prototyping
SENG8100

Description:

Adopting new software technologies is a risky proposition in many organizations, thankfully several styles of rapid prototyping and agile software development give us the flexibility to quickly evolve designs and evolve the fit of an emerging software prototype to meet the needs of early, exploration phases of adoption. Working as members of a software development team, students will gain practical experience and guidance to design and develop an effective software prototype.

  • Hours: 42
  • Credits: 3
  • Pre-Requisites:
  • CoRequisites:

Level 2

Course details

Co-op and Career Preparation
CEPR8200

Description:

This series of modules will prepare graduate certificate students for job searching for their co-op work terms with the guidance of a Co-op Advisor. Students will familiarize themselves with the co-operative education policies and procedures and will learn the expectations, rules, and regulations that apply in the workplace regarding social, organizational, ethical, and safety issues while deepening their awareness of self- reflective practices. Students will critically reflect on their skills, attitudes, and expectations and evaluate available opportunities in the workplace. Successful completion of these modules is a requirement for co-op eligibility.

  • Hours: 14
  • Credits: 1
  • Pre-Requisites:
  • CoRequisites:

Reinforcement Learning Programming
CSCN8020

Description:

Software development techniques are expanded on in this course which provides deeper coverage of machine learning implementations.

  • Hours: 42
  • Credits: 3
  • Pre-Requisites:
  • CoRequisites:

Artificial Intelligence for Business Decisions and Transformation 
CSCN8030

Description:

This course studies the implementation of AI in support of organizational decision making. With an overview of historic approaches to Decision Support Systems (DSS) the pathway to today’s predictive and autonomous applications for a range of industries is explored.

  • Hours: 42
  • Credits: 3
  • Pre-Requisites:
  • CoRequisites:

Case Studies in Artificial Intelligence and Machine Learning
CSCN8040

Description:

This course brings students through a moderated series of investigations into emerging and important trends related to AI and ML. Students share and discuss their findings on a range of related topics while developing and enhancing communications proficiencies.

  • Hours: 42
  • Credits: 3
  • Pre-Requisites:
  • CoRequisites:

Projects in Machine Learning 
INFO8665

Description:

This project course challenges students to fully apply the skills they have developed during this program towards the realization of a capstone project in AI and ML.

  • Hours: 126
  • Credits: 7
  • Pre-Requisites:
  • CoRequisites:

Applications in Industry 4.0
SENG8110

Description:

The fourth wave of industrialization, the era of ‘smart manufacturing’ is well underway. In this course students will study applications of AI and ML within the industrial and manufacturing spaces. With applications ranging from precision agriculture, autonomous manufacturing, deconstruction of e-waste, and even more applications, students learn to assess various hardware and software platforms helping to shape the manufacturing landscape of tomorrow.

  • Hours: 42
  • Credits: 3
  • Pre-Requisites:
  • CoRequisites:

Level 3

Course details

Co-op Work Term (Artificial Intelligence and Machine Learning)
COOP8210

Description:

This co-op work term will provide students with college-approved work experience in an authentic, professionally relevant work environment. Students will be provided the opportunity to connect theory and practice by leveraging their academic knowledge to develop specialized vocational skills. The practical applications of this work term will promote students’ awareness of key concepts and terminology in their field, improve their competencies in problem-solving and decision-making, further their application of professional judgement, hone their leadership skills (independently or as part of team), and enhance their capacity to critically analyze and reflect on their demonstrated abilities in the workplace.

  • Hours: 420
  • Credits: 14
  • Pre-Requisites: CEPR8200
  • CoRequisites:

Program outcomes

  1. Develop software solutions that incorporate Artificial Intelligence and Machine Learning technologies
  2. Evaluate the effectiveness and suitability of Artificial Intelligence and Machine Learning algorithms and programming languages
  3. Analyze Artificial Intelligence and Machine Learning project requirements in order to design prototype solutions
  4. Assess the effectiveness of prototype solutions and evolve them through structured software development processes
  5. Develop software prototypes using agile and rapid application development methodologies
  6. Communicate effectively to a variety of stakeholders as part of a software development team
  7. Enhance risk management policies in the organization by contributing to an assessment of data privacy and security issues related to Artificial Intelligence and Machine Learning applications
  8. Discuss emerging trends in Artificial Intelligence and Machine Learning and how they may impact everything from the daily lives of individuals to global economic and security development
  9. Design, develop, test, and present a major software project based on Artificial Intelligence and Machine Learning concepts taught in this program