Module Details |
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module. |
Title | Machine Learning in Practice | ||
Code | CSCK503 | ||
Coordinator |
Prof FP Coenen Computer Science Coenen@liverpool.ac.uk |
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Year | CATS Level | Semester | CATS Value |
Session 2020-21 | Level 7 FHEQ | Whole Session | 15 |
Aims |
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1. To provide an in-depth understanding of established techniques of machine learning, its real-world application and the legal contexts in which machine learning operates. 2. To provide students with comprehensive knowledge of the nature of data and the mechanism that may be used to pre-process data to support machine learning activities. 3. To establish a comprehensive and practical awareness of the techniques and metrics used to evaluate machine learning algorithms. 4. To furnish students with a in-depth and critical knowledge of a range of established approaches to machine learning, including their statistical and mathematical underpinning. 5. To provide a wide-ranging practical knowledge of an established machine learning workbench. |
Learning Outcomes |
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(M1) A well-founded and comprehensive knowledge of the operation of a widely used machine learning workbench. |
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(M2) A comprehensive and systematic understanding of the legal frameworks in which machine learning operates. |
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(M3) A practical ability to deploy effectively a variety of tools and techniques within the remit of machine learning. |
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(M4) A deep and systematic understanding of the limitations of a range of machine learning techniques and how the effectiveness of individual techniques can be analysed. |
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(S1) Communication skills in electronic as well as written form. |
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(S2) Self-direction and originality in tackling and solving problems. |
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(S3) An ability to act autonomously and professionally when planning and implementing solutions to computer science problems. |
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(S4) Experience of working in development teams, respecting others, co-operating, negotiating/persuading, awareness of interdependence with others. |
Syllabus |
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1. Machine learning fundamentals 2. Data Preprocessing 3. Dimensionality reduction 4. Linear regression 5. Classification 6. Decision Trees 7. Association Rule Mining 8. Clustering |
Teaching and Learning Strategies |
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The mode of delivery is by online learning, facilitated by a Virtual Learning Environment (VLE). This mode of study enables students to pursue modules via home study while continuing in employment. Module delivery involves the establishment of a virtual classroom in which a relatively small group of students (usually 10-25) work under the direction of a faculty member. Module delivery proceeds via a series of eight one-week online sessions, each of which comprises an online lecture, supported by other eLearning activities, posted electronically to a public folder in the virtual classroom. The eLearning activities will include lecture casts, live seminar sessions, self-assessment activities, reading materials and other multimedia resources. Communication within the virtual classroom is asynchronous, preserving the requirement that students are able to pursue the course in their own time, within the weekly time-frame of each seminar. An important element of the module provision is act ive learning through collaborative, cohort-based, learning using discussion fora where the students engage in assessed discussions facilitated by the faculty member responsible for the module. This in turn encourages both confidence and global citizenship (given the international nature of the online student body). |
Teaching Schedule |
Lectures | Seminars | Tutorials | Lab Practicals | Fieldwork Placement | Other | TOTAL | |
Study Hours |
24 |
40 |
64 | ||||
Timetable (if known) | |||||||
Private Study | 86 | ||||||
TOTAL HOURS | 150 |
Assessment |
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EXAM | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
CONTINUOUS | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
Group Presentation: Machine learning group project resulting in a demonstrable system and group video report (10 minutes) describing and analysing the approach and the results obtained. | 12 hours | 30 | ||||
Programming: Individual machine learning challenge resulting in a demonstrable system and supporting analysis in the form of a brief report (500 words). | 12 hours | 30 | ||||
Discussion Question 2: Participate actively in an online discussion concerning one of the applications of machine learning covered within the module, demonstrating an understanding of the key issues a | 1000-1500 words | 20 | ||||
Discussion Question 1: Participate actively in an online discussion concerning the background to machine learning, demonstrating an understanding of the key issues and showing original thought. | 1000-1500 words | 20 |
Recommended Texts |
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Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module. |