STATS 7014 - Statistics Topic B
North Terrace Campus - Semester 1 - 2019
-
General Course Information
Course Details
Course Code STATS 7014 Course Statistics Topic B Coordinating Unit Mathematical Sciences Term Semester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Available for Study Abroad and Exchange Y Assessment Ongoing assessment, exam Course Staff
Course Coordinator: Dr Jono Tuke
Course Timetable
The full timetable of all activities for this course can be accessed from .
-
Learning Outcomes
Course Learning Outcomes
In 2019, the topic of this course will be Advanced Data Science.
Syllabus:
This couse will follow closely the book, "An introduction to statistical learning with applications in R" by G. James et al. 2013. The topics covered will include:
1. Outline of statistical learning:
a. Estimating functions.
b. Prediction versus interpretability.
c. Supervised versus unsupervised.
d. Regression versus classification.
e. Bias-variance trade-off.
2. Linear regression:
a. Recap of simple linear regression.
b. Recap of multiple linear regression.
c. Extensions of the linear model.
d. Comparison to K-nearest neighbor.
3. Classification:
a. ROC curves.
b. Recap Logistic regression.
c. Linear discriminant analysis.
d. Quadratic discriminiant analysis.
e. Naive Bayes.
4. Resampling methods
a. Cross-validation.
b. Bootstraping
5. Linear model selection and regularization
a. Subset selection.
b. Ridge regression.
c. Lasso regression.
d. Principal components regression.
e. Partial least squares.
6. Non-linear functions
a. Step functions.
b. Regression splines.
c. Smoothing splines.
d. Local regression.
e. Generalised additive models.
7. Tree-based methods
a. Decision trees.
b. Classification trees.
c. Bagging.
d. Random forests.
e. Boosting.
8. Support Vector Machines
a. Maximal margin classifier.
b. Support vector classifiers.
c. Support vector machines.
9. Unsupervised learning:
a. Principal components analysis.
b. Clustering methods.
c. Multidimensional scaling
10. Parallel processing
a. MapReduce
Learning outcomes:
On successful completion of this course, students will:
1. Demonstrate an understanding of the foundational principles of machine learning
2. Recognise which method to use for a given data analysis problem.
3. Demonstrate an understanding the statistical underpinning of the chosen method.
4. Implement safely any chosen method and interpret the results.
5. Be confident to apply the methods to large datasets.
6. Apply the theory in the course to solve a range of problems at an appropriate level of difficulty.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
1, 2, 3, 4. Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
5 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
4, 6. Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
2 -
Learning Resources
Required Resources
There are no required resources for this course.Recommended Resources
- An introduction to statistical learning with applications in R, G.James et al. 2013 (available through canvas)
- The elements of statistical learning, T. Hastie et al. 2009 2nd Edition (available through canvas)
- Mining of massive datasets, Leskovec et al. 2014 2nd Edition (available through canvas)
Online Learning
The course will use the e-learning platform at University of Wollongong. -
Learning & Teaching Activities
Learning & Teaching Modes
The lecturer guides the students through the course material in 24 lectures. Students are expected to engage with the material in the lectures. Interaction with the lecturer and discussion of any difficulties that arise during the lecture is encouraged. Fortnightly assignments help students strengthen their understanding of the theory and practical work, and to help them gauge their progress. In the workshop, the lecturer along with the students will work through a guided analysis of a real dataset. In the workshop, the students will have hands-on experience with implementing the methods from the lectures.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload hours Lectures 24 72 Workshop 6 34 Assignments 5 50 Total 156 Learning Activities Summary
Lecture outline
Topic Lectures Outline of statistical learning: L1-2 Linear regression L3 Classification L4-5 Resampling methods L6 Linear model selection and regularization L7-10 Non-linear functions L11-14 Tree-based methods L15-18 Support Vector Machines L19 Unsupervised learning L20-22 Parallel processing L23-24
-
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
25% assignments, 5% presentation and 70% exam.Assessment Related Requirements
An aggregate score of at least 50% is required to pass the course.Assessment Detail
Assessment Distributed Due Weighting A1 Week 1 Friday Week 3 5% A2 Week 3 Friday Week 5 5% A3 Week 5 Friday Week 7 5% A4 Week 7 Friday Week 9 5% A5 Week 9 Friday Week 11 5% Presentation Week 6 Friday Week 12 5% Final exam Examination period 70% Submission
All assessment will be submitted electronically via canvas.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 .
-
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.
-
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
-
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
-
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鈥檚 disciplinary procedures.
The 成人大片 is committed to regular reviews of the courses and programs it offers to students. The 成人大片 therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.
