Adjunct Instructor, Programming Fundamentals Machine Learning Fundamentals

Description

The adjunct Instructor, programming fundamentals and machine learning methods is responsible for teaching courses in the computer science / data science curriculum.

The Programming Fundamentals courses are offered in the fall and spring. They cover essential Python programming skills.

Students will install and set up a programming environment for the Python software. Furthermore, students will learn basic concepts of programming and the syntax available in Python.

Flow charts and pseudocode will be used to introduce algorithmic concepts in programming. Students will learn variable types, create a program, and modify existing programs to perform simple data analysis and visualization.

At the end of the course, students will be prepared to perform moderate to complex programming tasks in Python. Emphasis will be placed on reading from a file, creating conditional statements, functions, loops, and iteration, and using basic data structures in Python, including Strings, Lists, Dictionaries, and Tuples.

Finally, students will learn skills to create programs as objects with multiple classes working together to perform a programming task or create a project using the object-oriented programming approach in Python and extensive use of the Structured Query Language (SQL).

The Machine Learning Methods course is offered both in Summer and Fall. It teaches students to apply linear algebra concepts in predictive modeling using Machine Learning techniques.

Topics will include applying probabilistic inference, matrices, and eigenvectors to solve predictive analytic problems. The strengths and limitations of several existing Machine Learning algorithms will be discussed.

Features engineering techniques will be taught. In addition, the course introduces deep learning algorithms and their ability to perform more than existing algorithms.

More importantly, students can modify existing Machine Learning algorithms for improved performance and develop new algorithms to meet new challenges.

Some of these courses may include some online components as well as some in-person, on-campus work. Ideally, one candidate would be adjunct faculty for both courses over an academic year.

Compensation is per course taught.

Responsibilities :

• Follow syllabus and instruct students in undergraduate programming courses while providing an inclusive, meaningful, and interactive in-class experience to support student learning outcomes
• Support our students by responding to student questions and concerns, holding office hours (can be virtual) for students.
• Utilize learning management system (D2L Brightspace) to support student learning, manage assignments, conduct assessments.
• Collaborate with an instructional designer, and other UHSP faculty as appropriate, to design a Machine Learning Fundamentals course that aligns with Data Science curricular competencies and UHSP course design best practice.

Qualifications

To succeed in this role, you will need :

Minimum Qualifications

• Master's degree in computer science, information technology, data science, or related field with demonstrated knowledge of course subject matter.
• Demonstrated expertise or experience teaching undergraduate students
• Demonstrated support for University Values of discovery, diversity, inclusion, integrity, respect and service.

Preferred Qualifications

• Proficiency using Python programming language
• Experience with Artificial Intelligence (AI), specifically applications of Machine Learning methods
• Proficiency with learning management systems such as D2L Brightspace
• Course development of undergraduate courses in an academic setting

If you are passionate about undergraduate education and possess the requisite knowledge and experience, we invite you to contribute to our mission.

Apply now if interested, upload a CV, and contact information for 3 references to UHSP's application system.

AA / EOE Statement :

University of Health Sciences & Pharmacy in St. Louis values the benefits of a diverse student body and workforce. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of race, color, religion, sex, national origin, sexual orientation, gender identity, protected veteran status or status as an individual with a disability.

The University's employment policy shall be supported through a continuing affirmative action commitment that embodies the standards of equal employment opportunities for all persons.

EOE / M / F / Veteran / Disabled.