COMP4702 Lecture 1

Lecture notes regarding the organisation of the course.

Marcus has introduced some background material, from which I have taken some notes:

Intro to ML

A subfield of Artificial Intelligence
- Goal: To create computer systems that are intelligent (strong), or fool you into thinking they are (weak)
- One thing we associate with (natural) intelligent entities: adaptive behaviour, and/or the ability to learn.
- So machine learning is about creating computer systems that can learn to perform tasks.
In other words, machine learning software automatically builds software to perform a task
- On the basis of data.
- So machine learning is about engineering algorithms that learn from data to solve interesting problems.
- The study of computer algorithms capable of learning to improve their performance on a task on the basis of their own experience.
- This is sometimes called inductive learning
Learning is an extremely broad concept, studied by psychologists, neuroscientists and others.
Machine Learning restricts itself to a few well-defined classes of learning problems that are still very general.
- Data-centred learning.
Furthermore, we concentrate mainly on problems that involve numerical data, or at least "structural" data.
- Nothing to do that involves language comprehension or high level cognitive-type stuff
- Our problems might be called sub-symbolic or low-level.
- structural data meaning that the input is converted to numerical data in some form.

Machine Learning (incorrectly aka Artificial Intelligence) is one of the hottest topics on the planet.
The surge in interest and attention in the last few years has been fast, frantic and exciting.
What is going on?
- The world has built a densely connected network of computers and devices which is widely available and relatively cheap.
- Technology has lead to an explosion in the collection, storage and availability of data (hence, data science, data analytics).
- There have been some big improvements in ML applications; tech giants have thrown huge amount of $ at AI/ML, everyone has started getting excited (and maybe afraid...)

How far can AI go? (Persona assistants, healthcare, self-driving cars, autonomous robots, language understanding)
What happens if technology displaces a large chunk of the labour force?
Autonomous weapons?
Data security?
Conscious machines?
The singularity?

Currently huge money, interest and effort in AI
Scientific rigour is incredibly important.
The objectives and applications of AI connect directly to issues of humanity such as ethics, bias, explainability, safety, trust, etc.
With critical mass, these factors can lead to pretty volatile situations and drama that computer scientists would not normally be involved in!

Assessment Task	Due Date	Weighting
Practical Demo	06 Mar - 26 May	18%
Report - Assignment	26 May	22%
Final Exam	Examination Period	60%

Demo: 2 demos in the semester, a timetable for when your demo is will be released at some point

Report: Due on the last day of the semester; Have the entire semester to work on the assignment.

Checkpoint for the assignment half way through (around wk7/8) and get feedback on your assignment.

Final Exam: Going back to paper-based final exam instead of take-home assignment format.

Similar to 2019-era style exam - short answer, calculations, longer response questions