CECS Professional Skills Mapping

COMP4680 — Advanced Topics in Machine Learning

Note: information provided here is indicative only. For full and current course information view the official page on P&C.

code:	COMP4680
name:	Advanced Topics in Machine Learning
unit value:	6
description:	This course explores a selected area relevant to statistical machine learning in depth, and will be taught by an SML staff member of internationally recognised standing and research interest in that area. Based on current SML staffing, this will be one of: • kernel methods • graphical models • reinforcement learning • convex analysis • optimisation • bioinformatics • minimal description length principle • topics in information theory • decision theory Over the past several years the content has alternated between “convex analysis and optimisation” and “structured probabilistic models”. Students should contact the course convenor to find out what topic is planned for the coming semester.
P&C:	https://programsandcourses.anu.edu.au/course/COMP4680
course learning outcomes:	Distinguish definitions of key concepts in convex analysis, including convexity of sets and functions, subgradients, and the convex dual Derive basic results about convex functions such as Jensen’s inequality Produce a formal optimization problem from a high-level description and determine whether the problem is convex Recognize standard convex optimization problems such as linear programs and quadratic programs Derive the standard (dual) quadratic program for support vector machines and understand the extension to max-margin methods for structured prediction Implement and analyse gradient descent algorithms such as stochastic gradient descent and mirror descent Differentiate the solution of an optimization problem with respect to its parameters
assessment:	Assignment 1 (8%) Assignment 2 (10%) Assignment 3 (8%) Assignment 4 (8%) Assignment 5 (8%) Assignment 6 (8%) Final Exam (50%)

Mapped learning outcomes

learning outcome	1. KNOWLEDGE AND SKILL BASE						2. ENGINEERING APPLICATION ABILITY				3. PROFESSIONAL AND PERSONAL ATTRIBUTES						assessment tasks
learning outcome	1.1	1.2	1.3	1.4	1.5	1.6	2.1	2.2	2.3	2.4	3.1	3.2	3.3	3.4	3.5	3.6	1	2	3	4	5	6	7
Distinguish definitions of key concepts in convex analysis, including convexity of sets and functions, subgradients, and the convex dual		✓	✓														✓	✓		✓		✓	✓
Derive basic results about convex functions such as Jensen’s inequality		✓	✓				✓											✓	✓			✓	✓
Produce a formal optimization problem from a high-level description and determine whether the problem is convex		✓	✓				✓												✓				✓
Recognize standard convex optimization problems such as linear programs and quadratic programs			✓																✓				✓
Derive the standard (dual) quadratic program for support vector machines and understand the extension to max-margin methods for structured prediction		✓	✓					✓												✓			✓
Implement and analyse gradient descent algorithms such as stochastic gradient descent and mirror descent		✓						✓													✓
Differentiate the solution of an optimization problem with respect to its parameters		✓	✓																			✓	✓

Course contribution towards the Engineers Australia Stage 1 Competency Standard

This table depicts the relative contribution of this course towards the Engineers Australia Stage 1 Competency Standard. Note that this illustration is indicative only, and may not take into account any recent changes to the course. You are advised to review the official course page on P&C for current information..

1. KNOWLEDGE AND SKILL BASE
1.1
1.2
1.3
1.4
1.5
1.6
2. ENGINEERING APPLICATION ABILITY
2.1
2.2
2.3
2.4
3. PROFESSIONAL AND PERSONAL ATTRIBUTES
3.1
3.2
3.3
3.4
3.5
3.6

Engineers Australia Stage 1 Competency Standard — summary

1. KNOWLEDGE AND SKILL BASE
	1.1	Comprehensive, theory based understanding of the underpinning natural and physical sciences and the engineering fundamentals applicable to the engineering discipline.
✓	1.2	Conceptual understanding of the, mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
✓	1.3	In depth understanding of specialist bodies of knowledge within the engineering discipline.
	1.4	Discernment of knowledge development and research directions within the engineering discipline.
	1.5	Knowledge of contextual factors impacting the engineering discipline.
	1.6	Understanding of the scope, principles, norms, accountabilities and bounds of contemporary engineering practice in the engineering discipline.
2. ENGINEERING APPLICATION ABILITY
✓	2.1	Application of established engineering methods to complex engineering problem solving.
✓	2.2	Fluent application of engineering techniques, tools and resources.
	2.3	Application of systematic engineering synthesis and design processes.
	2.4	Application of systematic approaches to the conduct and management of engineering projects.
3. PROFESSIONAL AND PERSONAL ATTRIBUTES
	3.1	Ethical conduct and professional accountability.
	3.2	Effective oral and written communication in professional and lay domains.
	3.3	Creative, innovative and pro-active demeanour.
	3.4	Professional use and management of information.
	3.5	Orderly management of self, and professional conduct.
	3.6	Effective team membership and team leadership.

Updated: 18 February 2021/ Responsible Officer: Dean, CECS/ Page Contact: CECS Academic Education Services