SL147686-Winter 2020-ENGG*6500 Introduction to Machine Learning

Sessional Lecturer Work Assignment
Sessional Lecturer, Unit 2
Academic Unit: 
School of Engineering
Semester(s) of Assignment(s): 
Winter 2020
Number of Available Work Assignment(s) / Sections: 
1
Level of Work Assignment(s): 
Other:
.50
Right of First Refusal (RoFR)
A Sessional Lecturer holds a RoFR (i.e., for a particular course) if they have successfully taught the course in the past six (6) semesters. A SL who holds a RoFR to this course is required to exercise their right by way of the online hiring system. Also see: What is Right of First Refusal (RoFR)?
A Sessional Lecturer Currently Holds a Right of First Refusal for this Course: 
Yes
Number of Assignments that Carry the Right of First Refusal: 
1
Course Details
Course Number: 
ENGG*6500
Course Name: 
Introduction to Machine Learning
Course Format: 
In-Class
Course Description: 
See Course Calendar
Other Course Description or Assignment Information: 
The aim of this course is to provide students with an introduction to algorithms and techniques of machine learning particularly in engineering applications. The emphasis will be on the fundamentals and not specific approach or software tool. Class discussions will cover and compare all current major approaches and their applicability to various engineering problems, while assignments and project will provide hands-on experience with some of the tools.
Projected Class Enrolment: 
40
Anticipated Duties and Responsibilities
Anticipated Duties and Responsibilities: 
Orientation-Training
Office Hours
Preparation
Student Consultation
Email Correspondence/Monitoring
Conducting Labs/Seminars
Invigilating Exams
Grading
Other Duties Described: 
Monitoring discussion forums and responding to posts.
Qualifications
Required Qualifications
Degree: 
PhD related to field
Prior Teaching Experience: 
Successful teaching related to field at college or university level
Required competence, capability, skill and ability related to course content: 
This position requires experience with scientific computing in Python, i.e. the Scientific Python stack (NumPy, Matplotlib, SciPy, Jupyter Notebooks). This position requires successful completion of ENGG*6500 or equivalent course during an engineering undergraduate or graduate degree or demonstrated teaching experience in an equivalent course.
Preferred Qualifications
Degree: 
Other
PhD in computer engineering, systems engineering, computer science, mathematics, statistics or related discipline
Prior Teaching Experience: 
Many years of successful teaching related to contents of the course.
Specific Preferred competence, capability, skill and ability related to course content: 
Ideal candidates will have a strong publication record in Machine Learning conferences and journals. Experience with modern machine learning / deep learning frameworks, e.g. PyTorch and TensorFlow is an asset
Days Required and Wages
Days and Times Required: 
Lecture Friday 2:30 PM - 5:20 PM
Period of the Work Agreement (Start Date and End Date): 
January 2, 2020 to April 27, 2020
Wages (per semester, per full-load): 
minimum $7,430.26 (effective 2019/20)
Other Posting Information
Application Deadline (All postings will automatically expire at 11:59 pm on this day): 
Thursday, December 19, 2019
Posting Email Contact: 
soe3913@uoguelph.ca
Hiring Contact Information: 
Bahram Gharabaghi, Associate Director of Graduate Studies, bgharaba@uoguelph.ca, THRN 2417, School of Engineering, University of Guelph, Guelph, Ontario, N1G 2W1 519-824-4120 ext 58451

At the University of Guelph, fostering a culture of inclusion is an institutional imperative. The University invites and encourages applications from all qualified individuals, including from groups that are traditionally underrepresented in employment, who may contribute to further diversification of our Institution. For more information, the Office of Diversity and Human Rights (DHR) is a welcoming, safe and confidential one-stop shop for information, training and support on issues relating to diversity and human rights on our campus.
SL work assignments are unionized with CUPE3913 and their terms and conditions of work are covered by the Unit 2 Collective Agreement between the University and CUPE 3913 (email contact: president@cupe3913.on.ca).

All applicants must be eligible to work in Canada specifically at the University of Guelph before applying for an academic work assignment. All successful applicants must reside in Ontario and must be able to attend on-campus in-person meetings as required