2024/5, Trimester 1, In Person,
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Occurrence: | 001 |
Primary mode of delivery: | In Person |
Location of delivery: | MERCHISTON |
Partner: | |
Member of staff responsible for delivering module: | Kehinde Babaagba |
Module Organiser: | |
Student Activity (Notional Equivalent Study Hours (NESH)) |
Mode of activity | Learning & Teaching Activity | NESH (Study Hours) | NESH Description |
Face To Face | Lecture | 20 | This will provide lecture-based teaching with interactive components, introducing students to the foundational concepts, algorithms and applications of machine learning providing both theoretical understanding and practical context. |
Face To Face | Tutorial | 4 | These tutorials on the fundamentals of machine learning will focus on the mathematical foundations, particularly linear algebra and calculus and will include hands-on problem solving, guided derivations, and practical examples linking mathematical concepts to core machine learning algorithms. |
Face To Face | Practical classes and workshops | 14 | This will provide practical classes focused on applying theoretical knowledge to real -word problems, fostering critical thinking and technical proficiency in machine learning fundamentals. This will include hands-on activities where students implement algorithms, analyze datasets and explore concepts like regression, classification and clustering using programming tools. |
Face To Face | Centrally Time Tabled Examination | 2 | The examination will provide a means to assess the fundamental machine learning concepts covered throughout the module. |
Online | Guided independent study | 160 | This involves students exploring key machine learning concepts through curated readings, exercises and practical coding tasks. These will encourage self-paced learning while being supported by periodic lecturer feedback and structured guidance. |
| Total Study Hours | 200 | |
| Expected Total Study Hours for Module | 200 | |
Assessment |
Type of Assessment | Weighting % | LOs covered | Week due | Length in Hours/Words | Description |
Practical Skills Assessment | 60 | 4~5~6 | Week 10 | HOURS= 4000 words | This will assess students’ ability to apply machine learning algorithms to a dataset, critically evaluate the results, and design, develop, and assess data-driven applications using Python. The assessment will require students to present their findings in a scientific format and draw well-reasoned, critical conclusions based on their analysis. |
Centrally Time Tabled Examination | 40 | 1~2~3 | Exam Period | HOURS= 2 | Centrally Time Tabled Examination |
Component 1 subtotal: | 60 | | |
Component 2 subtotal: | 40 | | | | |
Module subtotal: | 100 | | | | |