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# STAT30270

#### Statistical Machine Learning (STAT30270)

Subject:
Statistics & Actuarial Science
College:
Science
School:
Mathematics & Statistics
Level:
3 (Degree)
Credits:
5
Module Coordinator:
Dr Michael Fop
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No

Curricular information is subject to change.

Statistical machine learning encompasses a collection of techniques for discovering patterns in data and making predictions, involving models and methods at the intersection of machine learning and statistics. Geared towards introducing students to a diverse set of techniques for analyzing complex data, this module provides an overview of a variety of fundamental statistical machine learning methods for making predictions and discovering patterns in data. Emphasis is placed on understanding, critical evaluation, and the appropriate application of these techniques in diverse real-world data analysis scenarios. Additionally, the course also guides participants on implementing these statistical learning methods through the use of the R statistical software.

###### Learning Outcomes:

On completion of this module, students should have acquired the following skills:
- Have an understanding of the theory regarding all the statistical learning methods introduced.
- Being able to use the different techniques according to the context and the purpose of analysis.
- Being able to evaluate the performance of the statistical learning methods introduced.
- Use the statistical software R to implement these methods and being able to interpret the relevant output.

###### Indicative Module Content:

Supervised learning:
- Logistic regression for classification
- Tree-based and ensemble methods
- Support vector machines
- Evaluation of classifiers, model selection, and tuning

Unsupervised learning:
- Clustering
- Matrix factorization

Other topics.

###### Student Effort Hours:
Student Effort Type Hours
Lectures

24

Computer Aided Lab

11

Specified Learning Activities

25

Autonomous Student Learning

60

Total

120

###### Approaches to Teaching and Learning:
Lectures, tutorials, computer labs, enquiry and problem-based learning.
###### Requirements, Exclusions and Recommendations
Learning Requirements:

- Basic knowledge of linear algebra (vectors, vector spaces, matrices), calculus (derivatives), and function optimization.
- Basic understanding of statistical inference (confidence intervals, hypothesis testing, etc.) and familiarity with common probability distributions.
- Basic knowledge of regression analysis and linear models, including multiple linear regression.
- Familiarity with the R software for statistical computing and data programming.

Learning Recommendations:

- Students should have a knowledge of statistical inference at a level equivalent to that which would be achieved upon completion of "STAT20100 Inferential Statistics" or "STAT30280 Inference for Data Analytics (Onl)", or modules with similar contents and learning outcomes.
- Students should have a knowledge of data programming and analysis at a level equivalent to that which would be achieved upon completion of "STAT30340 Data Programming with R", and/or modules with a relevant component of coding and implementation of statistical methods with R.
- Knowledge of regression analysis and linear models to a level equivalent to that of "STAT20230 Modern Regression Analysis" or "STAT20240 Predictive Analytics" is beneficial.

###### Module Requisites and Incompatibles
Incompatibles:
FIN30520 - Machine Learning Finance, STAT40750 - Statistical Machine Lrn (OL)

###### Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): The assignment may include a mix of exercises, questions, code-based exercises, data analysis tasks. Week 10 Other No
30
No
Exam (In-person): End of term exam. End of trimester
Duration:
2 hr(s)
Alternative linear conversion grade scale 40% No
70
No

###### Carry forward of passed components
No

Resit In Terminal Exam
Summer Yes - 2 Hour
###### Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

###### How will my Feedback be Delivered?

Not yet recorded.

James, Witten, Tibshirani, Hastie - An Introduction to Statistical Learning with Applications in R - https://www.statlearning.com/

Hastie, Tibshirani, Friedman - The Elements of Statistical Learning - https://hastie.su.domains/ElemStatLearn/

Bishop - Pattern Recognition and Machine Learning - https://www.microsoft.com/en-us/research/publication/pattern-recognition-machine-learning/
Name Role
Mr Brian Buckley Tutor
Lapo Santi Tutor
Niyati Seth Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Fri 11:00 - 11:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 15:00 - 15:50
Spring Tutorial Offering 1 Week(s) - 22, 25, 30, 32 Thurs 17:00 - 17:50
Spring Tutorial Offering 2 Week(s) - 22, 25, 30, 32 Wed 17:00 - 17:50
Spring Tutorial Offering 3 Week(s) - 22, 25, 30, 32 Tues 17:00 - 17:50