menu

CECS Professional Skills Mapping

COMP3670 — Introduction to Machine Learning

code: COMP3670
name: Introduction to Machine Learning
unit value: 6
description: Essential foundations for any machine learning application are a basic statistical analysis of the data to be processed, a solid understanding of the mathematical foundations underpinning machine learning as well as the basic classes of learning/adaptation concepts. Those foundations are bundled in this single, introductory course to machine learning in preparation for deeper explorations into the topic, but also as a standalone unit.
P&C: https://programsandcourses.anu.edu.au/course/COMP3670
course learning outcomes:
  1. Develop an appreciation for what is involved in learning from data
  2. Understand basic data wrangling and data exploration
  3. Describe a variety of machine learning tasks: clustering, dimensionality reduction, regression and classification
  4. Understand how to formalise practical problems in terms of above tasks
  5. Interpret mathematical equations from linear algebra, vector calculus, statistics, and probability theory in terms of machine learning methods
  6. Understand how to perform evaluation of learning algorithms and model selection in practical problems
assessment:
  1. Assignment (40%)
  2. Exam (60%)

Mapped learning outcomes

learning outcome1. KNOWLEDGE AND SKILL BASE2. ENGINEERING APPLICATION ABILITY3. PROFESSIONAL AND PERSONAL ATTRIBUTESassessment tasks
1.11.21.31.41.51.62.12.22.32.43.13.23.33.43.53.612
  1. Develop an appreciation for what is involved in learning from data
✓✓✓✓
  1. Understand basic data wrangling and data exploration
✓✓✓
  1. Describe a variety of machine learning tasks: clustering, dimensionality reduction, regression and classification
✓✓✓
  1. Understand how to formalise practical problems in terms of above tasks
✓✓✓
  1. Interpret mathematical equations from linear algebra, vector calculus, statistics, and probability theory in terms of machine learning methods
✓✓✓
  1. Understand how to perform evaluation of learning algorithms and model selection in practical problems
✓✓✓✓

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

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