Course Title: Problem Solving and Algorithms
Part A: Course Overview
Course Title: Problem Solving and Algorithms
Credit Points: 12.00
Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
MATH2313 |
City Campus |
Undergraduate |
145H Mathematical & Geospatial Sciences |
Face-to-Face |
Sem 2 2016 |
MATH2313 |
City Campus |
Undergraduate |
171H School of Science |
Face-to-Face |
Sem 2 2017, Sem 2 2018, Sem 2 2019 |
Course Coordinator: Assoc Professor Marc Demange
Course Coordinator Phone: +61 3 9925 2385
Course Coordinator Email: marc.demange@rmit.edu.au
Course Coordinator Location: 8.9.13
Pre-requisite Courses and Assumed Knowledge and Capabilities
You are assumed to have completed MATH1150 Discrete Mathematics or equivalent
Course Description
This course develops skills in problem solving, using both mathematical and computer programming methods. It consists of two interdependent parts.
- Basic Problem Solving through Modelling. You will consider some unstructured problems (puzzles) and endeavour to develop relevant solutions through convincing mathematical analysis. Contrary to most of the mathematical problems you have already experienced, these problems are often incompletely specified and subject to interpretation, similar to most real-world problems you will encounter in your professional life. The notion of mathematical modelling will be addressed in depth.
- Basic Algorithms. Algorithms and their analyses are often helpful for finding solutions and building and/or validating an argument. They are as well essential for good computer programming. You will be introduced to some basic notions in algorithms, analyse them and discuss how they facilitate problem solving. You will consider lists, trees, sorting and searching and some examples of dynamic programming.
Practice exercises will improve your capabilities in using computer tools, in particular Excel, to solve problems and devise good solutions.
Objectives/Learning Outcomes/Capability Development
This course contributes to the following Program Learning Outcomes for BP083 Bachelor of Science (Mathematics) and BP245 Bachelor of Science (Statistics):
Knowledge and technical competence:
- use the appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.
Problem-solving:
- synthesise and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
- balance the complexity / accuracy of the mathematical / statistical models used and the timeliness of delivery of the solution.
Teamwork and project management
- contribute to professional work settings through effective participation in teams and organisation of project tasks
- constructively engage with other team members and resolve conflict.
Communication
- communicate both technical and non-technical material in a range of forms (written, electronic, graphic, oral) and tailor the style and means of communication to different audiences. Of particular interest is the ability to explain technical material, without unnecessary jargon, to lay persons such as the general public or line managers.
On completion of this course you should be able to:
- Engage and analyse unfamiliar problems at a basic level and identify and apply relevant strategies for solving them.
- Identify and apply basic concepts in algorithms and their analyses, design efficient algorithms for basic problems and verify their correctness;
- Select and use relevant software (Excel or programming environment) to devise and interpret experiments, either for analysing a new problem or for testing your method and comparing possible solutions;
- Produce convincing arguments to justify a strategy and critique the validity of others’ arguments.
- Communicate both technical and non-technical material in a range of forms (written, oral, electronic, graphic,) and work as a team member.
- Reflect on your own learning and that of your peers.
Overview of Learning Activities
Key concepts will be explained, discussed and illustrated in detail during the lectures.
Time during interactive lectures will be allowed for interactions and teamwork to develop critical thinking skills.
Supervised tutorials will build your capacity to use and apply the concepts for solving puzzles, to encourage you to think critically and analytically and provide feedback on academic progress.
Practice exercises will be devoted to illustrate the concepts using a programming environment and/or Excel.
The assessment will be a combination of hand-written work; computer generated output, group work and end-of-semester hand-written exam. All details will be provided in your Canvas and discussed in class.
Teacher Guided Hours: 48 per semester, and learner Directed Hours: 72 per semester
Overview of Learning Resources
All course material will be provided online through myRMIT Studies. These resources will include lecture notes on selected topics, slides, exercises, programs and spreadsheets.
Some additional supporting documents can be found at http://rmit.libguides.com/mathstats and http://rmit.libguides.com/compsci
Overview of Assessment
Assessment Tasks:
Assessment task 1: Early puzzle-based learning homework
Specifically assesses problem-solving skills.
Weighting 7.5%
This assessment task supports CLOs 1, 3, 6
Assessment task 2: Second puzzle-based learning homework
Specifically assesses problem-solving skills.
Weighting 7.5%
This assessment task supports CLOs 1, 3, 6
Assessment Task 3: Class test
Assesses your knowledge, the ability to apply it and key analytic and problem solving skills.
Weighting 15%
This assessment supports CLOs 1, 2, 4, 6
Assessment Task 4: Group project
A work prepared in-group assessing puzzle-based learning skills as well as your ability to work in a group, project planning and communication skills.
Weighting 25%
This assessment task supports CLOs 1, 2, 3, 4, 5, 6
Assessment Task 5: Exam
A two-hour exam
Weighting 45%
This assessment task supports CLOs 1, 2, 4