APTPUO-Fall 2025-MIA5100 A
APTPUO-Fall 2025-MIA5100 A
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Job Description
Location:
Main CampusSession:
2025 Fall Semester Trimestre dautomneFaculty:
Facult de gnie / Faculty of EngineeringUnit:
School of Engineering Design and Teaching InnovationPTCourse Title:
Foundations of Machine Learning (online)Course Code:
MIA 5100Section:
ACourse Description:
This course provides an in-depth exploration of the foundational topics in Machine Learning (ML) and Artificial Intelligence (AI) encompassing a broad range of concepts algorithms frameworks methodologies and practical applications. Topics will ranges from areas such as feature engineering supervised and unsupervised learning deep learning natural language processing and model deployment using state-of-art techniques. As part of this course students are expected to develop the skills and knowledge necessary to design implement evaluate ML models and deploy ML models effectively. Application areas will emphasize real-world contexts such as arts business social sciences and law domains. The course format includes lectures discussions and lab sessions to facilitate comprehensive learning.Posting limited to:
Professeur temps-partiel rgulier / Regular Part-Time ProfessorDate Posted:
May 22 2025Applications must be received BEFORE:
June 22 2025Expected Enrolment:
40Approval date:
May 22 2025Number of credits:
3Work Hours:
39Course type:
BPosting type:
Rgulier / RegularLanguage of instruction:
Anglais EnglishCompetence in second language:
ActiveCourse Schedule:
Jeudi Thursday 19:00-22:00 - -Requirements:
- Ph.D. in AI Machine Learning DTI Computer Science Statistics Mathematics Engineering or a related field.
- Demonstrated expertise in AI/Machine Learning with a general focus on areas such as Machine Learning Deep Learning Computer Vision Natural Language Processing (NLP) and model deployment including applications in real-world scenarios related to law business social sciences and arts.
- Teaching experience at the graduate and/or undergraduate level preferably in AI/Machine Learning or related fields.
- Hands-on experience with industry tools and technologies commonly used for developing and deploying Machine Learning Deep Learning and NLP algorithms such as Python TensorFlow PyTorch Scikit-Learn Transformers NLTK SpaCy Streamlit Flask etc.
Preference will be given to candidates with experience in project-based learning or experiential learningapproaches in AI/Machine Learning.
Additional Information and/or Comments:
An acceptable level of education and/or experience could be viewed as being equivalent to the educational required and/or demonstrated experience. If you are invited to continue the selection process please notify us of any adaptive measures you might require. Information you send us will be handled respectfully and in complete confidence. Employees are required under provincial law to successfully complete all mandatory legislated training. The list of training may be modified by provincial law.
The hiring process will be governed by the current APTPUO collective agreements; you can click here for the main unit here for the OLBI unit or here for the Toronto/Windsor unit to find out more.
The University of Ottawa embraces diversity and inclusion in the workplace. We are passionate about our people and committed to employment equity. We foster a culture of respect teamwork and inclusion where collaboration innovation and creativity fuel our quest for research and teaching excellence. While all qualified persons are invited to apply we welcome applications from qualified Indigenous persons racialized persons persons with disabilities women and LGBTQIA2S persons. The University is committed to creating and maintaining an accessible barrier-free work environment. The University is also committed to working with applicants with disabilities requesting accommodation during the recruitment assessment and selection processes. Applicants with disabilities may contact to communicate the accommodation need. All qualified candidates are encouraged to apply; however Canadians and permanent residents will be given priority.
Prior to May 1 2022 the University required all students faculty staff and visitors (including contractors) to be fully vaccinated against Covid-19 as defined in Policy 129 Covid-19 Vaccination. This policy was suspended effective May 1 2022 but may be reinstated at any point in the future depending on public health guidelines and the recommendations of experts.
Employment Type
Part-Time
