Items where Year is 2025

Spec for D4

Introducing Class, Sequence, Use Case, Activity and State Machine Diagrams

Full details to follow

Lab 4 COMP3207 2526

All lectures for week 2 in one file

Slides for COMP1312 Lecture 1.3 - Python basics: Variables, input & output

lab script and worksheet for twilight session, and data pack information

SCDTP UKRI Policy Internship 2024

lab script and worksheet

Revision

preparation for end of module CAA exam

extra material to support lecture 10.1

enums, number, var and ellipsis

Bonus lockdown lectures covering the non GUI material covered on nested classes and lambdas. May be useful if you are unclear on nested classes. Lectures delivered to a programming 2 module at UEA

A couple of videos of old lectures on the topics of Functors and Iterators in Java

overview of Maven and a first JavaFX project

Past exams paper for COMP2214.

Overview of design patterns and detailed look at the Functor, Iterator and Composite pattern

: Apart from what (little) OpenAI may be concealing from us, we all know (roughly) how Large Language Models (LLMs) such as ChatGPT work (their vast text databases, statistics, vector representations, and huge number of parameters, next-word training, etc.). However, none of us can say (hand on heart) that we are not surprised by what ChatGPT has proved to be able to do with these resources. This has even driven some of us to conclude that ChatGPT actually understands. It is not true that it understands. But it is also not true that we understand how it can do what it can do. I will suggest some hunches about benign “biases”—convergent constraints that emerge at the LLM scale that may be helping ChatGPT do so much better than we would have expected. These biases are inherent in the nature of language itself, at the LLM scale, and they are closely linked to what it is that ChatGPT lacks, which is direct sensorimotor grounding to connect its words to their referents and its propositions to their meanings. These convergent biases are related to (1) the parasitism of indirect verbal grounding on direct sensorimotor grounding, (2) the circularity of verbal definition, (3) the “mirroring” of language production and comprehension, (4) iconicity in propositions at LLM scale, (5) computational counterparts of human “categorical perception” in category learning by neural nets, and perhaps also (6) a conjecture by Chomsky about the laws of thought. The exposition will be in the form of a dialogue with ChatGPT-4.

Some examples

MULTI elevator coursework

SEG project specification (only for Biomedical Engineering students)

Inter-thread communication, producer-customer model

Synchronisation

Multi-threading 2

Multi-threading 1

static variables and static methods

Serialisation

file i/o, stream, char and byte based

interface, abstract, final, class diagram

Overriding

Super and Sub-classes, class diagram

public, private, getters, setters, class diagram

overloading

primitive VS reference

Intro to the module, OOP, Java, if-statement