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 | Maths and Statistics for Data Science and AI | ||
Code | CSCK544 | ||
Coordinator |
Professor FP Coenen Computer Science Coenen@liverpool.ac.uk |
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Year | CATS Level | Semester | CATS Value |
Session 2021-22 | Level 7 FHEQ | Whole Session | 15 |
Aims |
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1. To provide students with a systematic understanding of the key mathematical and statistical concepts and techniques underpinning established mechanisms of Data Science and AI. |
Learning Outcomes |
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(M1) A systematic understanding of basic mathematical principles and methods of interest to Data Science and AI. |
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(M2) A critical awareness of basic and more specialised concepts in probability theory and statistics relevant to Data Science and AI. |
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(M3) An ability to undertake software projects in the domain of Data Science and AI. |
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(M4) An ability to communicate the outcomes of experimental work in the domain of Data Science and AI. |
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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 within the domain of Computer Science, and an ability to act autonomously in planning and implementing solutions in a professional manner. |
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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. |
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(S5) Group working, respecting others, co-operating, negotiating/persuading, awareness of interdependence with others |
Syllabus |
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Weeks 1 and 2: Differential Calculus Weeks 5 and 6: Probability Theory Weeks 7 and 8: Statistics
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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 mode of learning includes a range of required and optional eLearning activities, including but not limited to: lecture casts, live seminars, self-assessment opportunities, and required and suggested further reading and try-for-yourself activities. Communication within the virtual classroom is asynchronous, preserving the requirement that students are able to pursue the module in their own time, within the weekly time-frame of each online session. An important element of the module provision is active 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 |
Practical Assessment 2. Practical exercise directed at probability theory and statistics | 1500-2250 words | 30 | ||||
Practical Assessment 1. Practical exercise directed at differential Calculus and linear algebra. | 1500-2250 words | 30 | ||||
Discussion Question 2: Use the online discussion forum to critically discuss experiences and opinion within the cohort relating to some aspect of maths and statistics in Data Science and AI . | 1000-1500 words | 20 | ||||
Discussion Question 1: Use the online discussion forum to critically discuss experiences and opinion within the cohort relating to some aspect of maths and statistics in Data Science and AI . | 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. |