FM1AZML75 Programming for Machine Learning
FM1AZML75 Programming for Machine Learning
- Course description
- Course codeFM1AZML75
- Level of study5.1
- Program of studyApplied Machine Learning
- Credits7.5
- Course coordinatorLeon Grobbelaar
The course provides knowledge of programming and skills to create code, format and manipulate data and program testing. Candidates learn logical and scientific approaches to programming for Machine Learning applications and problem-solving issues in their own code.
This course is relevant to the program because programming skill is essential for many machine-learning applications. This course will expose the students to the complexities of working with various kinds of libraries for data formatting and machine learning.
The candidate:
- has knowledge of concepts and processes that are used to read, format and manipulate data
- has knowledge of processes and tools that are used to describe a data-driven machine-learning application problem
- has knowledge of methodologies and processes that are used in data-driven machine learning programming problem decomposition
- can update his/her knowledge of data manipulation and program testing
The candidate:
- can apply knowledge of machine learning and related applications to identify and solve problems in the code
- masters relevant tools and techniques to create machine learning applications
- can find information relevant to programming design and machine learning applications
- can study software and application designs and identify potential vulnerabilities in the functionalities of the program and what measures need to be implemented
The candidate:
- has developed a logical and scientific approach relative to programming for machine learning
- can develop programming concepts for machine learning applications
In this course, the following teaching and learning methods can be applied, but are not limited to:
- Lecture: Educator-led presentations or activities providing knowledge, skills, or general competencies in the subject area.
- Group work: Collaborative activities where students work together to solve problems or complete tasks.
- Tutoring: One-on-one or small group sessions with an instructor for personalized guidance and support.
- Student presentations: Opportunities for students to demonstrate their understanding of course material by presenting to peers.
- Online lessons: Digital content delivered via an online learning platform.
- Guidance: Individualized advice and direction from instructors to support students in their learning journey.
- Workshops: Practical sessions focused on hands-on application of theoretical concepts or skills.
- Self-study: Independent study where students engage with course material on their own without any teacher support.
| Form of assessment | Grading scale | Grouping | Duration of assessment |
|---|---|---|---|
Course Assignment | Pass / Fail | Group/Individual | 5 Week(s) |