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 Dr S Wild
Computer Science
Sebastian.Wild@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2021-22 Level 7 FHEQ Second Semester 15

Aims

The main aim of this module is to lay a strong foundation for research in the field of algorithms, with a clear emphasis on algorithmic problems and solutions that haven proven useful in applications (e.g., in bioinformatics, search engines, networks, and data compression). This is done through the rigorous study of selected algorithmic techniques, an in-depth, systematic, and critical discussion of their respective benefits and weaknesses (by means of mathematical and empirical analysis), and by gaining hands-on experience on solving new algorithmic challenges residing on the border of the theory of abstract algorithms and engineering of applied algorithmic solutions.

Learning Outcomes

(LO1) Be able to recognize standard algorithmic problems, apply and judge known solutions based on comprehensive and in-depth understanding of their properties and limitations.

(LO2) Be able to systematically compare the goals and approaches in algorithm theory and algorithm engineering.

(LO3) Be able to critically assess algorithmic solutions from the research literature and to adapt these solutions to a range of application scenarios.

(LO4) Be able to design algorithmic solutions for real-world applications in small-scale programming projects.

(LO5) Be able to critically communicate algorithmic problems and solutions (both within and outside of the algorithms/computer science community).

(S1) Critical thinking and problem solving - Critical analysis

(S2) Critical thinking and problem solving - Problem identification

(S3) Critical thinking and problem solving - Evaluation

(S4) Critical thinking and problem solving - Creative thinking

(S5) Numeracy/computational skills - Problem solving

Syllabus
 

Foundations [2 weeks]: Study of mathematical models of computation (including parallel computation), methods for the analysis of algorithms, and fundamental data structures.

Parallel Algorithms [3 weeks]: Primitives of PRAM algorithms, efficient sequential and parallel solutions for exemplary algorithmic problems, for example, sorting and string matching.

2 In-Depth Topics [4 weeks]: Advanced algorithms and data structures for an exemplary algorithmic problem, for example, text indexing and lossless data compression, laying the foundation for understanding and using current state-of-the-art methods, including critical discussion of research results.

Bonus Research Topic [1 week]: An example of an advanced algorithm or data structure close to current research topics, for example range-minimum queries, and its applications.

Exemplary weekly schedule:

Unit 0 – Administrativa & Proof Techniques
Unit 1 – Machines & Models
Unit 2 – Fundamental Data Structures
Unit 3 – Efficient Sorting
Unit 4 – String Matching
Unit 5 – Parallel String Matching
Unit 6 – Text indexing
Unit 7 – Compression
Unit 8 – Error-Correcting Codes
Unit 9 – Range-minimum queries

Teaching and Learning Strategies

Teaching Method 1 - Lecture
Description:
Attendance Recorded: Not yet decided

Teaching Method 2 - Tutorial
Description:
Attendance Recorded: Not yet decided

Due to Covid-19, in 2021/22, one or more of the following delivery methods will be implemented based on the current local conditions.
(a) Hybrid delivery
Teaching Method 1 - Lecture
Description: Mix of on-campus/on-line synchronous/asynchronous sessions
Teaching Method 2 - Tutorial
Description: Mix of on-campus/on-line synchronous/asynchronous sessions

(b) Fully online delivery and assessment
Teaching Method 1 - Lecture
Description: On-line synchronous/asynchronous lectures
Teaching Method 2 - Tutorial
Description: On-line synchronous/asynchronous sessions

(c) Standard on-campus delivery
Teaching Method 1 - Lecture
Description: Mix of on-campus/on-line synchronous/asynchronous sessions
Teaching Method 2 - Tutorial
Description: On-campus synchronous sessions

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
(526) Written Exam There is a resit opportunity. This is an anonymous assessment. Assessment Schedule (When): Semester 2  250 minutes    50       
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
(526.1) Video presentation. There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When): Semester 2      10       
(526.2) Programming Puzzle 1. There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When): Semester 2      10       
(526.3) Programming Puzzle 2. There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When): Semester 2      10       
(526.4) In-class quizzes There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When): Semester 2      15       
(526.5) Class Discussion Participation.      5       

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