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
- Study Plan 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
Digital Learning Resources
The learning management system (LMS) is the primary learning platform where students access most of their course materials. The content is presented in various formats, such as text, images, models, videos or podcasts. Each course follows a progression plan, designed to lead students through weekly modules at their own pace. Exercises and assignments (individual or in groups) are embedded throughout the courses to support continuous practice and assessment of the learning outcomes.
Campus Resources
In addition to the digital learning resources, campus students participate in physical learning activities led by teachers as part of the overall delivery.
Guidance
Guidance and feedback from teachers support students' learning journeys, and may be provided synchronously or asynchronously, individually or in groups, via text, video or in-person feedback.
| Form of assessment | Grading scale | Grouping | Duration of assessment |
|---|---|---|---|
Course Assignment | Pass / Fail | Group/Individual | 5 Week(s) |