MATHS 1008 - Mathematics for Information Technology I
North Terrace Campus - Semester 2 - 2016
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General Course Information
Course Details
Course Code MATHS 1008 Course Mathematics for Information Technology I Coordinating Unit Mathematical Sciences Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 5 hours per week Available for Study Abroad and Exchange Y Prerequisites SACE Stage 2 Mathematical Studies Assessment ongoing assessment 30%, exam 70% Course Staff
Course Coordinator: Dr Adrian Koerber
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
On successful completion of this course students will be able to:- Demonstrate understanding of concepts in discrete mathematics, probability and cryptography.
- Employ methods related to these concepts in a variety of applications.
- Apply logical thinking to problem solving in context.
- Use appropriate technology to aid problem solving.
- Demonstrate skills in writing mathematics.
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
all Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
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Learning Resources
Required Resources
Mathematics for Information Technology I student notes.Recommended Resources
- Ross, K. A. & Wright, C. R. B., Discrete Mathematics, Prentice Hall
- Johnsonbaugh, R., Discrete Mathematics (7th ed), Prentice Hall
- Goodman, R., An introduction to stochastic models, Benjamin-Cummings
- Ross, S., Introduction to probability models (7th ed), Academic Press
Online Learning
This course uses MyUni exclusively for providing electronic resources, such as lecture notes, assignment papers, and sample solutions. Students should make appropriate use of these resources. Link to MyUni login page: https://myuni.adelaide.edu.au/webapps/login/ -
Learning & Teaching Activities
Learning & Teaching Modes
This course relies on lectures to guide students through the material, tutorial classes to provide students with class/small group/individual assistance, and a sequence of written and online assignments to provide formative assessment opportunities for students to practice techniques and develop their understanding of the course.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload hours Lectures 48 72 Tutorials 11 22 Assignments 11 55 Mid Semester Test 1 7 Total 156 Learning Activities Summary
The two topics of discrete mathematics and probability detailed below are taught in parallel, with two lectures a week on each. The tutorials are a combination of the two topics, pertaining to the previous week's lectures. Note that some sections only loosely fall into the categories of discrete mathematics or probability but are so listed to indicate the stream they are taught in.
Lecture Outline
Discrete Mathematics- Sets and relations, equivalence relations, functions. (4 lectures)
- Logic, predicate caclulus. (2 lectures)
- Types of argument. (2 lectures)
- Switching circuits. (2 lectures)
- Graphs, trees, spanning trees, Kruskal's algorithm, binary search trees. (5 lectures)
- Mathematical induction. (3 lectures)
- Cryptosysytems, Caesar cipher, Hill cipher. (2 lectures)
- Elementary number theory. (2 lectures)
- Public key cryptography, the mathematics of the RSA algorithm. (2 lectures)
- Sample spaces, events, inclusion-exclusion. (3 lectures)
- Conditional probablility and the product rule. (1 lecture)
- Probability trees, independent events, Bayes' Formula, Law of Total Probability. (2 lectures)
- Discrete random variables and probability distributions. (6 lectures)
- Counting techniques. (6 lectures)
- Linear homogeneous recurrence relations with constant coefficients. (6 lectures)
Tutorial 1: Sets, relations, Venn diagrams, simple probability.
Tutorial 2: Functions, 1-1, onto. Conditional probability.
Tutorial 3: Propositions, truth tables. Probability tree diagrams.
Tutorial 4: Types of argument, negation. Bayes' Theorem, Law of Total Probability.
Tutorial 5: Boolean expressions, circuit diagrams. Permutation, combinations, counting arguments.
Tutorial 6: Mathematical induction, recursive definitions. Partitions. Binomial probability.
Tutorial 7: Graphs, trees, paths. Binomial proability distribution.
Tutorial 8: Kruskal's algorithm. Binary search trees. Mode, median. Normal distribution.
Tutorial 9: Binary search trees. Caesar cipher. Markov chains.
Tutorial 10: Hill cipher, elementary number theory. Recurrence relations.
Tutorial 11: Congruence, Fermat's theorem. Inhomogeneous recurrence relations.
Note: Precise tutorial content may vary due to the vagaries of public holidays.
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Assessment
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Summary
Assessment Task Task Type Weighting Learning Outcomes Assignments Formative 15% all Mid Semester Test Summative and Formative 15% 1,2,3 Exam Summative 70% 1,2,3,5 Assessment Related Requirements
An aggregate score of 50% is required to pass the course. Furthermore students must achieve at least 45% on the final examination to pass the course.Assessment Detail
Assessment item Distributed Due date Weighting Assignment 1 week 1 week 3 1.4% Assignment 2 week 2 week 4 1.4% Assignment 3 week 3 week 5 1.4% Assignment 4 week 4 week 6 1.4% Assignment 5 week 5 week 7 1.4% Assignment 6 week 6 week 8 1.4% Assignment 7 week 7 week 9 1.4% Assignment 8 week 8 week 10 1.4% Assignment 9 week 9 week 11 1.4% Assignment 10 week 10 week 12 1.4% Assignment 11 week 11 week 13 1.4% Mid Semester Test week 8 15% Submission
- All written assignments are to be submitted at the designated time and place with a signed cover sheet attached.
- Late assignments will not be accepted without a medical certificate.
- Written assignments will have a one week turn-around time for feedback to students.
Course Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through .
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Student Feedback
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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Fraud Awareness
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