Cognitive Science Modules

This is the proposed timetable for 2016/2017. All details are tentative and subject to change. The final timetable will be published in September 2016.

Students typically take eight of the ten modules listed below. All module choices must be discussed with the course director. Substitutions on an individual basis may be possible.

First semester, Autumn 2016
Mon Tue Wed Thurs Fri
9:00-11:00

PSY 40020 Funadamentals of Cognitive Neuropsychology

Ciara Greene
Arts E114
  11:00-13:00

COMP 40250
Introduction to Cognitive Psychology

Fintan Costello
CSI B1.08
   
11:00 - 13:00

COMP 47230
Introduction to Cognitive Science

Fred Cummins
CSI B1.09
  14:00-16:00

PHIL 40010
Philosophy of Mind

Markus Scholsser
D522, School of Philosophy, Newman Building.
   
Second semester, Spring 2017
10:00-11:00

PSY 40550
Readings in Visual and Social Cognition

Nuala Brady
Arts. Th O.
  10:00-11:00

PSY 40550
Readings in Visual and Social Cognition

Nuala Brady
Arts, Th O.
   
11:00-13:00

COMP 40260
Connectionism and Dynamical Systems

Fred Cummins
CSI B1.08
  11:00-13:00

COMP 40270
Cognitive Modelling

Fintan Costello
CSI B1.08
11:00-13:00

PHIL41380
Dealing with Disagreement

Maria Baghramian
Place to be announced.
 
14:00-16:00

COMP 40260
Connectionism and Dynamical Systems

Lab
Fred Cummins
CSI B1.08
  14:00-16:00

COMP 40280
Topics in Cognitive Science: Post-Cognitivist Seminar

Fred Cummins
CSI Coffee Area Meeting Room

COMP 40630
Human Computer Interaction

David Coyle
H1.51 (Science Hub)
 

Non-core modules of possible relevance

Depending on the background of individual students, some substitutions to the standard suite of modules may be appropriate. Some modules of possible relevance are listed here, but this list does not try to be exhaustive.

STAT 40430 Biostatistics
Statistics module for those without any statistical background.
Semester 1, Tuesday 13:00-14:00 (Science Hub H2.18), and Friday, 13:00-14:00 (B005, Health Sciences). Students also take one tutorial from the following: Wednesday (14:00-15:00, Science Hub, H2.18), Thursday (13:00-14:00, Science Hub, H2.22) or Wednesday (18:00-19:00, Engineering 135).
Lecturer: Dr Brendan Murphy.
COMP 41450 Advanced Machine Learning
The objective of this module is to provide students with a familiarity with the core concepts in Machine Learning. Key techniques in supervised and unsupervised machine learning will be covered. This module has a practical focus and students will be expected to complete three practical assignments. One assignment will involve the implementation of a machine learning algorithm in a general programming language such as C++ or Java.This module requires significant mathematical ability as some of the algorithms require an understanding of matrix decomposition techniques. In addition the evaluation of the performance of machine learning algorithms requires an understanding of statistical significance testing.
Semester 1: Tuesday, 9:00-10:00 and Thursday, 9:00-10-00, CSI 002
Lecturer: Prof. Padraig Cunningham