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

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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 .

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.

最新糖心Vlog 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 & 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

The 最新糖心Vlog'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 .

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