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 APPLIED ALGORITHMICS
Code COMP526
Coordinator Prof LA Gasieniec
Computer Science
L.A.Gasieniec@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2016-17 Level 7 FHEQ Second Semester 15

Aims

The main aim of this module is to lay down a strong context for research explorations in the field of algorithms. This is done through a rigorous study of selected algorithmic solutions with application to related fields requiring analysis of large data (bioinformatics, networking, data compression, etc). This will be done by provision of the rationale for the use of algorithmic design and analysis methods, and also an in-depth, systematic and critical study of several important algorithmic challenges residing on the border of the theory of abstract algorithms and engineering of applied algorithmic solutions.


Learning Outcomes

Critical awareness of algorithmic problems and as well as research issues in the context of engineering of efficient algorithmic solutions.

Clear understanding of the relation (including differences) between the goals in the design of efficient abstract and applied algorithmic solutions.

Ability to understand and assimilate research literature relating to the application of algorithmic techniques.

Ability to undertake small software projects.

Ability to communicate (within and outside of Algorithms/CS community) problems related to efficiency of algorithmic solutions

Syllabus
1 

Study of standard computational (including parallel) models, algorithmic methods, solutions and methods of analysis used in theory of algorithms and experimental algorithmics. This includes critical study of exemplar algorithmic problems including sorting, pattern matching, and others.

[3 weeks]

　

Study of time, space and communication efficient algorithms and data structures for large centralised and distributed environments. This includes studies on respective solutions for peer-to-peer systems, crowdsourcing, data compression, security and others.

[2 weeks]

　

Study of novel computational models motivated by new challenges in streaming of large data, memory caching, external memory computations, dynamic networks, and others.

[2 weeks]

　

Study of more advanced applied algorithms used in networks (e.g., communication, random walks, significance, clustering), property/groups testing, error correction, visualisation and others.

[3weeks]

Teaching and Learning Strategies

Lecture -

Tutorial -


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
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Unseen Written Exam  150  Semester 2  75  Yes  Standard UoL penalty applies  Written Exam Notes (applying to all assessments) During the semester there are four assessment (programming assignments) tasks. This work is not marked anonymously. This is followed by a proper written examination marked anonymously.  
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Coursework  Each CA requiring 5-  Semester 2  8  Yes  Standard UoL penalty applies  Programming exercise 1 
Coursework  Each CA requiring 5-  Semester 2  8  Yes  Standard UoL penalty applies  Programming exercise 2 
Coursework  Each CA requiring 5-  Semester 2  9  Yes  Standard UoL penalty applies  Programming exercise 3 

Recommended Texts
Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.
Explanation of Reading List: