Computer AlgebraRegular and context-free grammars; Finite state machines; Turing Machines; computability; recursive functions; lambda calculus; functional programming languages; correctness of imperative and functional programs.
To give an insight into what can be computed and how algorithms can be described and proven.
Introduction to the use of Computer Algebra software in Pure Mathematics and other disciplines. Students are taught to use Mathematica to solve a wide variety of problems.
It is intended that students shall, on successful completion of the module, be able to: perform routine calculations using Mathematica and examine the results critically, modifying Mathematica's default settings if necessary to obtain correct results; use Mathematica to assist in the investigation of realistic mathematical problems, possibly using features from several different areas of Mathematica.
Yes, four years though one of the years is a work placement. The two modules appear to be the only two specific maths modules.Is that it for four years? Only one of those is actually a maths course. CS students should be capable of figuring out Mathematica on their own...
Data Structures; cartesian products, discriminated unions, sets, sequences, trees and graphs; sequential and indexed sequential file models; recursive backtracking; sparse and recursive data structures
To enable the identification and design of appropriate data abstractions and their related operations and to develop an efficient implementation in a nominated programming language.
I think set theory and logic would be included in "discreet mathematics" that you would learn in a good comp sci degree, - at least according to my OH who is a computer scientist
Artificial IntelligenceA rigorous approach to software development. Logical foundations. Specification of data types. Implicit and direct specification of functions and operations. Reasoning about specifications, refinement, axiomatic semantics.
To present a scientific approach to the construction of software systems.
Course Contents
Module introduction: artificial intelligence - definition, scope, successes and limitations. Logic, propositional calculus, predicate calculus, inference; fuzzy logic; logical programming, PROLOG. Expert Systems, knowledge, domains, rules, inference engine, forward and backward chaining, tree searching, probability and certainty, combining fuzzy facts, apriori probability, applications. Information retrieval and disambiguation. Introduction to pattern recognition and neural networks. Linguistics, grammar, surface structure, deep structure, structure representations, transformations, lexical decomposition, n-gram models, classification, domains. Speech recognition.
Knowledge and understanding of techniques and selected software relevant to the field of artificial intelligence. Ability to identify techniques relevant to particular problems in artificial intelligence. Ability to discuss and provide proofs for basic rules used in artificial intelligence. Ability to identify opportunities for software solutions. Ability to interrogate a knowledge base in PROLOG. Ability to understand and use natural language grammars. Ability to solve specific problems using the rules of artificial intelligence e.g. in pattern recognition.
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