Module Catalogues, Xi'an Jiaotong-Liverpool University   
Module Code: CSE102
Module Title: Algorithmic Foundations and Problem Solving
Module Level: Level 1
Module Credits: 5.00
Academic Year: 2019/20
Semester: SEM2
Originating Department: Computer Science and Software Engineering
Pre-requisites: N/A
1. To introduce the notation, terminology, and techniques underpinning the study of algorithms.
2. To introduce the standard algorithmic design paradigms employed in the development of efficient algorithmic solutions.
3. To introduce the mathematical tools needed for the analysis of algorithms in terms of the use of formal models of Time and Space
Learning outcomes 
At the end of this module students should be able to:

1. describe standard algorithms such as sorting algorithms, search algorithms, string matching algorithms, graph traversal of algorithms;

2. apply these algorithms or a given pseudo code algorithm in order to solve a given problem;

3. carry out simple asymptotic analyses of algorithms involving sequence, selection, and iteration, and identify and compare simple properties of these algorithms;

4. describe the algorithm design principles of divide-and-conquer, greedy method, and dynamic programming and distinguish the differences between these principles;

5. apply the studied algorithms that illustrate these design principles;

6. apply the studied design principles to produce algorithmic solutions to a given problem; and

7. explain and illustrate the distinction between different classes of problems, in particular, polynomial time and exponential time solvable problems.
Method of teaching and learning 
In each normal week, students will be expected to attend a three-hour formal lecture and to participate in a one-hour supervised problem class. Lectures will introduce students to the academic content and practical skills which are the subject of the module, while problem classes will allow students to practice those skills. In addition, students will be expected to devote 8 hours of unsupervised time for private study. Private study will provide time for reflection and consideration of lecture material and background reading. Two assessments will be used to test to what extent practical skills have been learnt. A written examination at the end of the module will assess the academic achievement of students.
1. Introduction (8 lectures)

Definition of an algorithm, counting elementary operations during execution, worst-case analysis of running time and storage requirements--examples of simple algorithms will be discussed in detail. Design of pseudo code algorithms.

2. Complexity Issues (6 lectures)

Asymptotic and “order of” notation for complexity. Comparison of polynomial time and exponential time complexities and examples of algorithms with such complexities. Brief introduction of the notion of computable and non-computable functions.

3. Review of Graphs structures and their representation (4 lectures)

Directed and Undirected graphs; trees; representation by adjacency matrices and incidence lists, graph and tree traversals.

4. Algorithm Design Techniques (21 lectures)

Review of the standard algorithm design paradigms commonly used in Computer Science together with typical example problems solved by these.

a) Overview: why a range of design methods is needed.

b) Divide-and-Conquer algorithms: general overview of approach; run-time analysis of simple Divide and-Conquer methods via solution of recurrence relations.

c) Dynamic Programming: differences from Divide-and-Conquer; general overview; necessity for iterative implementation.

d) Greedy Method: concept of optimisation problem and the distinction between ‘exact’ and ‘approximate’ solution algorithms.
Delivery Hours  
Lectures Seminars Tutorials Lab/Prcaticals Fieldwork / Placement Other(Private study) Total
Hours/Semester 39     13      98  150 


Sequence Method % of Final Mark
1 Problem Solving 10.00
2 Problem Solving Cw 10.00
3 Online Open Book Exam (2 Hours) 80.00

Module Catalogue generated from SITS CUT-OFF: 5/30/2020 5:06:45 AM