STATS 7014 - Statistics Topic B

North Terrace Campus - Semester 1 - 2019

Please contact the School of Mathematical Sciences for further details, or view course information on the School of Mathematical Sciences web site at http://www.maths.adelaide.edu.au

• General Course Information
Course Details
Course Code STATS 7014 Statistics Topic B Mathematical Sciences Semester 1 Postgraduate Coursework North Terrace Campus 3 Y 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 Course Planner.

• 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.

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.

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
• 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
1. An introduction to statistical learning with applications in R, G.James et al. 2013 (available through canvas)
2. The elements of statistical learning, T. Hastie et al. 2009 2nd Edition (available through canvas)
3. 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.

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:

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
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.

Grades for your performance in this course will be awarded in accordance with the following scheme:

M10 (Coursework Mark Scheme)
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.

• Student Feedback

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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
• Policies & Guidelines
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