ETC3250 Introduction to Machine Learning
Difficulty:
Year Completed: Semester 1, 2024
Prerequisite: ETC2420 or ETC2560
Exemption: N/A (prerequisite for other units)
Mean Setu Score: 77.1%
Clarity of Learning Outcomes: 80%
Clarity of Assessments: 77.2%
Feedback: 69.6%
Resources: 76%
Engagement: 85.8%
Satisfaction: 74%
Subject Content:
Lecture(s) and Tutorial(s):
Textbook(s):
Assessments:
Select and develop appropriate models for clustering, prediction or classification.
Estimate and simulate from a variety of statistical models.
Measure the uncertainty of a prediction or classification using resampling methods.
Apply business analytic tools to produce innovative solution in finance, marketing, economics and related areas.
Manage very large data sets in a modern software environment.
R was used to cover the content of this unit
1 x 2 hour lecture (Zoom)
1 x 1.5 hour tutorial
Introduction to Statistical Learning
Interactively exploring high-dimensional data and models in R (by Dianne Cook and Ursula Laa)
Weekly Quizzes: 3%
Assignment 1: 9%
Assignment 2: 9%
Assignment 3: 9%
Project: 10%
Final Exam: 60%
Comments
The course offers an in-depth exploration into various modelling techniques such as linear, generalized, mixed random effects, and non-parametric modelling. It emphasizes continuous improvement in data modelling strategies, making it highly relevant for those interested in programming and statistical modelling. Proficiency in R is essential for grasping the practical application of these concepts, as the unit heavily relies on this software.
Lectures are engaging and provide a solid foundation for understanding complex modelling techniques. It is recommended to review lecture slides before attending to enhance comprehension and keep pace with the material presented (presented on Zoom). The lecturers play a crucial role in clarifying doubts and ensuring that students grasp the concepts effectively, and in order to do well in quizzes.
Tutorials, while not mandatory, offer a wealth of content and a fresh perspective on statistical analysis. They are particularly beneficial for developing good research practices and improving R programming skills, especially in data cleaning and modelling. Attending tutorials and actively participating can significantly enhance one's understanding and application of the machine learning models and to assist with assignments.
The course assessment includes three practical assignments based on R programming and a theoretical exam. The assignments are well-structured to apply theoretical knowledge to practical scenarios, whereas the exam focuses on the theoretical underpinnings of the models taught. Attention to the marking rubric post-assessment is advisable to understand and rectify any minor errors, with tutors being responsive to any concerns raised.
This course is commendable for its structured approach, blending theoretical knowledge with practical application. It is especially beneficial for those looking to apply statistical modeling in various fields, not limited to actuarial science. The course's emphasis on practical assignments, coupled with the support and engagement from lecturers and tutorials, makes it a worthwhile endeavour for anyone looking to deepen their understanding and application of statistical models.
Attending lectures promptly and take quizzes immediately after consolidating knowledge. Utilize tutorials and consultations effectively, especially for (R-based) assignments).
General Overview:
Lectures:
Tutorials:
Assessments/Other Assessments:
Concluding Remarks