The unit is being introduced as a core unit in the redeveloped Bachelor of Computer Science degree as part of the Monash Course Architecture initiative for implementation in 2016.

June 2017: Amendment to prerequisites after discussion with Arun Konagurthu and David Albrecht. Remove FIT2102 from prerequisites. Effective 2018 (first offering in S2 2018). 15 August 2017: Amendment to the exam length and marks to conform to the faculty policy. 02 July 2018: Updated the synopsis to reflect more accurately the topics to be handled in this unit --Effective S2 2018 (first offering). 24 October 2018: Updated contact hours -- Merging tute (previously, 1hr fortnightly) and lab (previously, 2hr weekly) activities into a single 3 hour weekly activity where the first hour will be tutorial and the next two hours will be lab work. This way the material between tutes and labs can be made to interact more effectively for the benefit of students' learning.

20/9/2019: Admin - updating exam duration to include additional 10 minutes as per University requirement.

30/01/20: Admin - updating the on-campus workload requirements to align with scheduled activity types. Also minor amendment to wording of workload.

21/09/2020 Admin: Update to include new assessment and teaching approach fields as per Handbook requirements.

This is a core unit in the Bachelor of Computer Science degree, Advanced Computer Science specialisation.

At the completion of this unit students should be able to:

1. analyse efficient data structures and effective algorithmic paradigms;
2. design and implement efficient algorithms and data structures for use on large data sets;
3. apply advanced algorithms and data structures to tackle complex computational problems;
4. prove the correctness of programs and reason about their space and time complexities.

020109 (50%), 020111 (50%)

This unit builds on the concepts learnt in introductory algorithms and data structures study. It covers advanced algorithmic paradigms and problem-solving techniques required to address real-world programming challenges. It explores, in depth, the design and analysis of space-efficient data structures and time-efficient problem solving strategies to be used with them. Topics include amortized analysis, advanced sorting and searching algorithms, new tree/string/graph data structures and algorithms, and number-theoretic algorithms amongst others.

Technological requirements

On-campus

Lecture and Problem-based learning :

Examination (2 hours and 10 minutes): 60%; In-semester assessment: 40%

1. Assignment 1: - 10% ULO: 1, 2, 3, 4
2. Assignment 2: - 10% ULO: 1, 2, 3, 4
3. Assignment 3: - 20% ULO: 1, 2, 3, 4
4. Final examination: - 60% ULO: 1, 2, 3, 4

Minimum total expected workload equals 12 hours per week comprising: (a.) Contact hours for on-campus students:

• Two hours lectures weekly
• Three hours Laboratory work weekly

• A minimum of 8 hours independent study per week for completing lab work and assignments, private study and revision. }

FIT2004