MATHS 1008 - Mathematics for Information Technology I

North Terrace Campus - Semester 2 - 2018

This course provides an introduction to a number of areas of discrete mathematics with wide applicability. Areas of application include: computer logic, analysis of algorithms, telecommunications, gambling and public key cryptography. In addition it introduces a number of fundamental concepts which are useful in Statistics, Computer Science and further studies in Mathematics. Topics covered are: Discrete mathematics: sets, relations, logic, graphs, mathematical induction and difference equations; probability and permutations and combinations; information security and encryption: prime numbers, congruences.

  • 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 Methods (formerly 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 Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course students will be able to:
    1. Demonstrate understanding of concepts in discrete mathematics, probability and cryptography.
    2. Employ methods related to these concepts in a variety of applications.
    3. Apply logical thinking to problem solving in context.
    4. Use appropriate technology to aid problem solving.
    5. 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)
    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
  • Learning Resources
    Recommended Resources
    1. Ross, K. A. & Wright, C. R. B., Discrete Mathematics, Prentice Hall
    2. Johnsonbaugh, R., Discrete Mathematics (7th ed), Prentice Hall
    3. Goodman, R., An introduction to stochastic models, Benjamin-Cummings
    4. Ross, S., Introduction to probability models (7th ed), Academic Press
    Online Learning
    This course uses MyUni extensively and exclusively for providing electronic resources, such as lecture notes, assignment and tutorial questions, and worked solutions. Students should make appropriate use of these resources.
    MyUni can be accessed here:

    Students are also reminded that they need to check their University email on a daily basis. Sometimes important and time-critical information might be sent by email and students are expected to have read it. Any problems with accessing or managing student email accounts should be directed to Technology Services.
  • 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.

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    Activity Quantity  Workload hours
    Lectures 48 84
    Tutorials 11 11
    Assignments and Practice 11 55
    Mid Semester Test 1 6
    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. (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)

  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. 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
    Assignments are due every fortnight, the first is released in Week 1 and due in Week 3.

    Tutorials are weekly beginning in Week 2.

    The Mid Semester Test occurs in your enrolled computer lab in Week 8.

    Precise details of all of these will be provided on the MyUni site for this course.
    1. All written assignments are to be e-submitted following the instructions on MyUni.
    2. Late assignments will not be accepted without a medical certificate.
    3. Written assignments will have a one week turn-around time for feedback to students.
    See MyUni for more comprehensive details regarding assignment submission, our late policy etc.
    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 Access Adelaide.

    Replacement and Additional Assessment Examinations (R/AA Exams)

    Students are encouraged to read the University's R/AA exam information on the University’s Examinations webpage here:
  • 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 ( 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.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

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