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 | Advanced Algorithmic Techniques | ||
Code | COMP523 | ||
Coordinator |
Dr A Filos-Ratsikas Computer Science Aris.Filos-Ratsikas@liverpool.ac.uk |
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Year | CATS Level | Semester | CATS Value |
Session 2021-22 | Level 7 FHEQ | First Semester | 15 |
Aims |
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To provide a sound foundation concerning the design and analysis of advanaced discrete algorithms. |
Learning Outcomes |
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(LO1) Describe the following classes of algorithms and design principles associated with them: recursive algorithms, graph (search-based) algorithms, greedy algorithms, algorithms based on dynamic programming, network flow (optimisation) algorithms, approximation algorithms, randomised algorithms, distributed and parallel algorithms. |
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(LO2) Illustrate the above mentioned classes by examples from classical algorithmic areas, current research and applications. |
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(LO3) Identify which of the studied design principles are used in a given algorithm taking account of the similarities and differences between the principles. |
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(LO4) Apply the studied design principles to produce efficient algorithmic solutions to a given problem taking account of the strengths and weaknesses of the applicable principles. |
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(LO5) Outline methods of analysing correctness and asymptotic performance of the studied classes of algorithms, and apply them to analyse correctness and asymptotic performance of a given algorithm. |
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(S1) Critical thinking and problem solving - Critical analysis |
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(S2) Critical thinking and problem solving - Evaluation |
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(S3) Critical thinking and problem solving - Problem identification |
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(S4) Critical thinking and problem solving - Creative thinking |
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(S5) Numeracy/computational skills - Reason with numbers/mathematical concepts |
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(S6) Numeracy/computational skills - Problem solving |
Syllabus |
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Core algorithmic primitives including: notation, standard (sequential) models of computation, algorithmic design methods, data structures, formal proofs and methods of analysis on the example of recent (up to date) research problems. (2 weeks) Advanced particular graph algorithms, string algorithms, randomised algorithms, approximation algorithms, on-line algorithms. (5 weeks) Crucial elements of probabilistic theory. (1 week) Non-standard computational models including: parallel, distributed, non-deterministic (incl. probabilistic), biologically motivated, and appropriate diverse measures of complexity. (2 weeks) |
Teaching and Learning Strategies |
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Teaching Method 1 - Lecture Due to Covid-19, in 2021/22, one or more of the following delivery methods will be implemented based on the current local conditions. (b) Fully online delivery and assessment (c) Standard on-campus delivery |
Teaching Schedule |
Lectures | Seminars | Tutorials | Lab Practicals | Fieldwork Placement | Other | TOTAL | |
Study Hours |
30 |
10 |
40 | ||||
Timetable (if known) | |||||||
Private Study | 110 | ||||||
TOTAL HOURS | 150 |
Assessment |
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EXAM | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
(523) Final exam | 70 | |||||
CONTINUOUS | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
(523.1) Assessment 1 There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Semester 1 | 36 hours expected fo | 15 | ||||
(523.2) Assessment 2 There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Semester 1 | 36 hours expected fo | 15 |
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. |