Bullinaria, John - School of Computer Science, University of Birmingham

• Neural Network Ensembles for Time Series Forecasting V. Landassuri-Moreno
• Nature Inspired Genetic Algorithms for Hard Packing Problems Philipp Rohlfshagen & John A. Bullinaria
• In: Proceedings of the Cognitive Science of Natural Language Processing Conference, Dublin City University, 1996. Ambiguous Data in Connectionist Language Processing Models
• Research Skills Advanced MSc and PhD Students
• Artificial Intelligence Techniques A 1st year undergraduate degrees with AI
• AITA -Exercise Sheet 6 This week we have two questions on Production Systems taken from past exam papers, plus a
• Connectionist Modelling of Spelling John A. Bullinaria
• Lesioned Networks As Models Of Neuropsychological De cits
• Overview of More Advanced Topics Introduction to Neural Networks : Lecture 19
• Introduction to Neural Computation Neural Computation
• Feature Selection in Evolved Artificial Neural Networks using the Evolutionary Algorithm EPNet
• IAI : The Roots, Goals and Sub-fields of AI John A. Bullinaria, 2005
• Interacting Agent Based Systems Dean Petters
• Psychology, measurements
• The Evolution of Variable Learning Rates John A. Bullinaria
• BinaryMappings feedforward
• Design and Media Team 2009 Assessment Specification
• Proceedings of 1993 International Joint Conference on Neural Networks Neural Network Models of Reading Multi-SyllabicWords
• IAI Exercise Sheet 1 This first set of exercise questions lead you into some well known philosophical questions
• AITA -Exercise Sheet 4 This week we have a set of questions about knowledge representations. They should be read
• Modelling the Acquisition of Reading Skills John A. Bullinaria
• AITA : Knowledge Representation John A. Bullinaria, 2003
• Foundations of Computer Science (Semester 2) 2011 Assessed Exercise Sheet 8 10% of Continuous Assessment Mark
• Committee Machines Neural Computation : Lecture 19
• Extracting Semantic Representations from Large Text Corpora
• Neural Computation : Important Equations John A. Bullinaria -2010
• Radial Basis Function Networks: Algorithms Introduction to Neural Networks : Lecture 13
• The Generalized Delta Rule and Practical Considerations Neural Computation : Lecture 6
• The Generalized Delta Rule and Practical Considerations Introduction to Neural Networks : Lecture 6
• Modelling Lexical Decision: Who needs a lexicon? John A. Bullinaria
• Self Organizing Maps: Algorithms and Applications Introduction to Neural Networks : Lecture 17
• Neural Comptation : Revision Lectures John A. Bullinaria, 2010
• AITA -Exercise Sheet 9 The aim of this exercise session is to gain some experience in the practical aspects of
• Introduction to Artificial Intelligence 1st year undergraduate degrees with AI and/or CS
• Learning in Multi-Layer Perceptrons, Back-Propagation Introduction to Neural Networks : Lecture 7
• Extracting Semantic Representations from Large Text Corpora
• IAI : Production Systems John A. Bullinaria, 2005
• Connectionist Modelling of Reading Aloud John Bullinaria
• Learning Vector Quantization (LVQ) Introduction to Neural Networks : Lecture 18
• Radial Basis Function Networks: Applications Introduction to Neural Networks : Lecture 14
• AITA -Exercise Sheet 7 This week we have a set of five questions about search. They should be read in conjunction
• Foundations of Computer Science (Semester 2) 2011 Assessed Exercise Sheet 4 10% of Continuous Assessment Mark
• Modelling the Acquisition of Reading Skills John A. Bullinaria
• Exploring the Baldwin Effect in Evolving Adaptable Control Systems
• Connectionist Neuropsychology
• AITA : Uninformed Search John A. Bullinaria, 2003
• Biasing the Evolution of Modular Neural Networks V. Landassuri-Moreno
• Recurrent Neural Networks Neural Computation : Lecture 12
• Introduction to Neural Networks : Exercise Sheet 3 John A. Bullinaria -2004
• SEXUAL SELECTION WITH COMPETITIVE/CO-OPERATIVE OPERATORS FOR GENETIC ALGORITHMS
• IAI -Exercise Sheet 3 This week we have a set of questions about copying the intelligence and other capabilities of
• IAI : Knowledge Representation John A. Bullinaria, 2005
• From Biological Models to the Evolution of Robot Control Systems
• Modelling Lexical Decision Using Corpus Derived Semantic Representations
• Edinburgh University Technical Report --May 1994 Learning the Past Tense of English Verbs
• BinaryMappings feedforward
• Design and Media Team 2009 Suggested Milestones
• Hebbian Learning and Gradient Descent Learning Introduction to Neural Networks : Lecture 5
• Edinburgh University Technical Report May 1994 Learning the Past Tense of English Verbs
• 2-4 September, Birmingham Registration Form
• DISSOCIATION CONNECTIONIST
• Generational SteadyState
• Biological Style Evolution of Robot Control Systems John A. Bullinaria
• Mixture Models and Model Selection Neural Computation : Lecture 20
• Alternative Splicing in Evolutionary Computation: Adaptation in Dynamic Environments
• IAI : Search John A. Bullinaria, 2005
• ToModularize ToModularize?
• THE EVOLUTION OF GATED SUB-NETWORKS AND MODULARITY IN THE HUMAN BRAIN
• AI in Computer Chess Colin Frayn,
• A brief introduction to LATEX Chris Bowers
• IAI -Exercise Sheet 9 This week we have a set of questions about Expert Systems. They should be read in
• Neural Computation : Exercise Sheet 2 John A. Bullinaria -2010
• Foundations of Computer Science (Semester 2) 2011 Assessed Exercise Sheet 5 10% of Continuous Assessment Mark
• Applications of Multi-Layer Perceptrons Introduction to Neural Networks : Lecture 11
• AITA -Exercise Sheet 3 This sheet should be read in conjunction with your notes and handouts for lecture w4s1. We
• Neural Computation : Exercise Sheet 1 John A. Bullinaria -2010
• AITA : Brain Modelling John A. Bullinaria, 2003
• A brief introduction to LATEX Chris Bowers
• IAI : Overview and Revision John A. Bullinaria, 2006
• NEUROEVOLUTION OF AUTO-TEACHING ARCHITECTURES EDWARD ROBINSON & JOHN A. BULLINARIA
• Ensemble Techniques for Avoiding Poor Performance in Evolved Neural Networks
• Research Skills Advanced MSc and PhD Students
• Simulating the Evolution of Modular Neural Systems John A. Bullinaria (j.bullinaria@physics.org)
• Foundations of Computer Science (Semester 2) 2011 Assessed Exercise Sheet 3 10% of Continuous Assessment Mark
• Understanding the Advantages of Modularity in Neural Systems John A. Bullinaria (j.a.bullinaria@cs.bham.ac.uk)
• Foundations of Computer Science (Semester 2) 2011 Assessed Exercise Sheet 7 10% of Continuous Assessment Mark
• Learning and Evolution of Control Systems John A. Bullinaria & Patricia M. Riddell
• Regularization in Oculomotor Adaptation John A. Bullinaria, Patricia M. Riddell, Simon K. Rushton
• Foundations of Computer Science (Semester 2) 2011 Assessed Exercise Sheet 4 10% of Continuous Assessment Mark
• Neural Computation : Exercise Sheet 4 John A. Bullinaria -2010
• IAI -Exercise Sheet 8 This week we have a set of nine questions about search. They should be read in conjunction
• A Genetic Algorithm with Exon Shuffling Crossover for Hard Bin Packing Problems
• USING ENRICHED SEMANTIC REPRESENTATIONS IN PREDICTIONS OF HUMAN BRAIN ACTIVITY
• An improved multi-objective evolutionary algorithm for the vehicle routing problem with time windows
• Lifetime Learning as a Factor in Life History Evolution John A. Bullinaria
• The Importance of Neurophysiological Constraints for Modelling the Emergence of Modularity
• A Multi-Objective Density Restricted Genetic Algorithm for the Vehicle Routing Problem with Time Windows
• Semantic Categorization Using Simple Word Co-occurrence Statistics John A. Bullinaria
• NEURAL MODELS OF HEAD-DIRECTION CELLS PETER ZEIDMAN JOHN A. BULLINARIA
• ANOTHER REASON WHY WE SHOULD LOOK AFTER OUR CHILDREN
• Extracting Semantic Representations from Word Co-occurrence Statistics: A Computational Study
• ExGA II: An Improved Exonic Genetic Algorithm for the Multiple Knapsack Problem
• An Introduction to Computational Intelligence Techniques for Robot Control
• Evolved Age Dependent Plasticity Improves Neural Network Performance John A. Bullinaria
• EMBRYOLOGICAL MODELLING OF THE EVOLUTION OF NEURAL ARCHITECTURE
• EVOLVING NEURAL NETWORKS THAT SUFFER MINIMAL CATASTROPHIC FORGETTING
• Connectionist Neuropsychology John A. Bullinaria
• Generational versus Steady-State Evolution for Optimizing Neural Network Learning
• ON THE EVOLUTION OF IRRATIONAL BEHAVIOUR JOHN A. BULLINARIA
• Evolving Efficient Learning Algorithms for Binary Mappings John A. Bullinaria
• Optimizing the Learning of Binary Mappings John A. Bullinaria
• Incorporating Developmental Factors into Models of Accommodation and Vergence
• Explorations in the Derivation of Semantic Representations from Word Co-occurrence Statistics
• Analyzing the Internal Representations of Trained Neural Networks
• Modelling Lexical Decision Using Corpus Derived Semantic Representations
• Modelling Reaction Times John A. Bullinaria
• Foundations of Computer Science (Semester 2) 2011 Assessed Exercise Sheet 1 10% of Continuous Assessment Mark
• Foundations of Computer Science (Semester 2) 2011 Assessed Exercise Sheet 1 10% of Continuous Assessment Mark
• Foundations of Computer Science (Semester 2) 2011 Assessed Exercise Sheet 2 10% of Continuous Assessment Mark
• Foundations of Computer Science (Semester 2) 2011 Assessed Exercise Sheet 3 10% of Continuous Assessment Mark
• Foundations of Computer Science (Semester 2) 2011 Assessed Exercise Sheet 5 10% of Continuous Assessment Mark
• Foundations of Computer Science (Semester 2) 2011 Assessed Exercise Sheet 6 10% of Continuous Assessment Mark
• Foundations of Computer Science (Semester 2) 2011 Assessed Exercise Sheet 7 10% of Continuous Assessment Mark
• Foundations of Computer Science (Semester 2) 2011 Assessed Exercise Sheet 8 10% of Continuous Assessment Mark
• Foundations of Computer Science (Semester 2) 2011 Assessed Exercise Sheet 9 10% of Continuous Assessment Mark
• Foundations of Computer Science (Semester 2) 2011 Assessed Exercise Sheet 10 10% of Continuous Assessment Mark
• Foundations of Computer Science (Semester 2) 2011 Assessed Exercise Sheet 10 10% of Continuous Assessment Mark
• Biological Neurons and Neural Networks, Artificial Neurons Neural Computation : Lecture 2
• Learning in Multi-Layer Perceptrons -Back-Propagation Neural Computation : Lecture 7
• Learning with Momentum, Conjugate Gradient Learning Neural Computation : Lecture 8
• Bias and Variance, Under-Fitting and Over-Fitting Neural Computation : Lecture 9
• Radial Basis Function Networks: Introduction Neural Computation : Lecture 13
• Radial Basis Function Networks: Applications Neural Computation : Lecture 15
• Self Organizing Maps: Fundamentals Neural Computation : Lecture 16
• Self Organizing Maps: Algorithms and Applications Neural Computation : Lecture 17
• Neural Computation : Exercise Sheet 5 John A. Bullinaria -2010
• Neural Computation : Exercise Sheet 6 John A. Bullinaria -2010
• Research Skills Advanced MSc and PhD Students
• A brief introduction to LATEX Chris Bowers
• Research Skills Advanced MSc and PhD Students
• IAI : Biological Intelligence and Neural Networks John A. Bullinaria, 2005
• IAI -Exercise Sheet 4 This sheet should be read in conjunction with your lecture notes and handouts for Week 4.
• IAI : Semantic Networks and Frames John A. Bullinaria, 2005
• IAI -Exercise Sheet 5 This week we have a set of questions about knowledge representations. They should be read
• IAI -Exercise Sheet 6 This week we have a set of questions about semantic networks and frame based systems.
• IAI : Expert Systems John A. Bullinaria, 2005
• IAI : Treatment of Uncertainty John A. Bullinaria, 2005
• IAI -Exercise Sheet 10 This week we have a set of exam style questions about the treatment of uncertainty and
• Introduction to Neural Networks 2nd Year UG, MSc in Computer Science
• Networks of Artificial Neurons, Single Layer Perceptrons Introduction to Neural Networks : Lecture 3
• Learning and Generalization in Single Layer Perceptrons Introduction to Neural Networks : Lecture 4
• Learning with Momentum, Conjugate Gradient Learning Introduction to Neural Networks : Lecture 8
• Bias and Variance, Under-Fitting and Over-Fitting Introduction to Neural Networks : Lecture 9
• Improving Generalization Introduction to Neural Networks : Lecture 10
• Radial Basis Function Networks: Introduction Introduction to Neural Networks : Lecture 12
• Committee Machines Introduction to Neural Networks : Lecture 15
• Self Organizing Maps: Fundamentals Introduction to Neural Networks : Lecture 16
• Introduction to Neural Networks : Exercise Sheet 1 John A. Bullinaria -2004
• Introduction to Neural Networks : Exercise Sheet 4 John A. Bullinaria -2004
• Introduction to Neural Networks : Exercise Sheet 5 John A. Bullinaria -2004
• AITA : The Roots, Goals and Subfields of AI John A. Bullinaria, 2003
• AITA : Biological Intelligence and Neural Networks John A. Bullinaria, 2003
• AITA Exercise Sheet 1 This first set of exercise questions lead you into some well known philosophical questions
• AITA : Building Intelligent Agents John A. Bullinaria, 2003
• AITA : Semantic Networks John A. Bullinaria, 2003
• AITA : Frame Based Systems John A. Bullinaria, 2003
• AI Techniques A : Continuous Assessment Assignment John A. Bullinaria -2003
• AITA : Expert Systems John A. Bullinaria, 2003
• AITA -Exercise Sheet 8 The following four questions came up as the AITA half of the 3 hour AI Techniques
• AITA : Limitations and Misconceptions of AI John A. Bullinaria, 2003
• IAI : Building Intelligent Agents John A. Bullinaria, 2005
• Proceedings Conference,
• Classifiers Birmingham,
• Explorations in the Derivation of Semantic Representations from Word Cooccurrence Statistics
• ON THE EVOLUTION OF IRRATIONAL BEHAVIOUR JOHNA. BULLINARIA
• Research Skills Advanced MSc and PhD Students
• The Effect of Learning on Life History Evolution John A. Bullinaria
• Introduction to Neural Networks : Exercise Sheet 2 John A. Bullinaria -2004
• Gendered Selection Strategies in Genetic Algorithms for Optimization
• IAI -Exercise Sheet 7 This week we have a series of questions on Production Systems: two questions taken from
• Bi-objective Optimization for the Vehicle Routing Problem with Time Windows
• Improving Generalization Neural Computation : Lecture 10
• AITA Exercise Sheet 2 Here we begin with five questions concerning the underlying definitions of AI, and then have
• Biological Neurons and Neural Networks, Artificial Neurons Introduction to Neural Networks : Lecture 2
• Connectionist Dissociations,
• Incorporating Developmental Factors into Models of Accommodation and Vergence
• Noise Reduction by Multi-Target Learning John A. Bullinaria
• Hebbian Learning and Gradient Descent Learning Neural Computation : Lecture 5
• AITA : Machine Learning John A. Bullinaria, 2003
• Neural Computation : Exercise Sheet 3 John A. Bullinaria -2010
• Learning and Generalization in Single Layer Perceptrons Neural Computation : Lecture 4
• Introduction to Neural Networks : Revision Lectures John A. Bullinaria, 2004
• Gendered Selection Strategies in Genetic Algorithms for Optimization
• Applications of Multi-Layer Perceptrons Neural Computation : Lecture 11
• Research Skills Advanced MSc and PhD Students
• Evolving Improved Incremental Learning Schemes for Neural Network Systems
• Optimizing Delivery Time in Multi-Objective Vehicle Routing Problems with Time Windows
• Evolving Variable Plasticity in Neural Systems JOHN A. BULLINARIA
• DISSOCIATION IN CONNECTIONIST SYSTEMS John A. Bullinaria
• Comparison of Similarity Measures for the Multi-Objective Vehicle Routing Problem with Time Windows
• An Exonic Genetic Algorithm with RNA Editing Inspired Repair Function for the Multiple Knapsack Problem
• Connectionist Dissociations, Confounding Factors and Modularity
• Foundations of Computer Science (Semester 2) 2011 Assessed Exercise Sheet 6 10% of Continuous Assessment Mark
• Memes in Artificial Life Simulations of Life History Evolution John A. Bullinaria
• Introduction to Neural Networks : Continuous Assessment Project John A. Bullinaria -2004
• Research Skills Advanced MSc and PhD Students
• Research Skills Advanced MSc and PhD Students
• To Modularize or Not To Modularize? John A. Bullinaria
• Introduction to Neural Networks : Important Equations John A. Bullinaria -2004
• Radial Basis Function Networks: Algorithms Neural Computation : Lecture 14
• Learning Vector Quantization Neural Computation : Lecture 18
• Research Skills Lecture 16: Intellectual Property Rights
• SEXUAL SELECTION WITH COMPETITIVE/COOPERATIVE OPERATORS FOR GENETIC ALGORITHMS
• Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward-based Scenarios
• Research Skills Advanced MSc and PhD Students
• IAI Exercise Sheet 2 This week we have a set of questions about the definitions of AI, a question about evolution
• AITA : Treatment of Uncertainty John A. Bullinaria, 2003
• IAI : Machine Learning John A. Bullinaria, 2005
• Modelling Lexical Decision: Who needs a lexicon? John A. Bullinaria
• Regularization in Oculomotor Adaptation John A. Bullinaria, Patricia M. Riddell, Simon K. Rushton
• Instructions Affiliation
• Review of Ian Glynn, 1999, "An Anatomy of Thought" in Perception, 29, 377-378, 2000 Evolving Thoughts on Thought and its Evolution
• Using Evolution to Improve Neural Network Learning: Pitfalls and Solutions
• Simulating the Evolution of Modular Neural Systems John A. Bullinaria (j.bullinaria@physics.org)
• Evolving Efficient Neural Network Classifiers John A. Bullinaria
• EVOLVED DUAL WEIGHT NEURAL ARCHITECTURES TO FACILITATE INCREMENTAL LEARNING
• Implicit Alternative Splicing for Genetic Algorithms Philipp Rohlfshagen and John A. Bullinaria
• Abstract--A common problem in speech technology is the alignment of representations of text and phonemes, and the
• Evolving Neural Networks: Is it Really Worth the Effort? John A. Bullinaria
• The Evolution of Minimal Catastrophic Forgetting in Neural Systems Tebogo Seipone (T.Seipone@cs.bham.ac.uk)
• AITA : Production Systems John A. Bullinaria, 2003
• Foundations of Computer Science (Semester 2) 2011 Assessed Exercise Sheet 2 10% of Continuous Assessment Mark
• Noise Reduction by MultiTarget Learning John A. Bullinaria
• Neural Network Applications P.Tino@cs.bham.ac.uk
• Research Skills Advanced MSc and PhD Students
• Evolving Variable Plasticity in Neural Systems JOHN A. BULLINARIA
• Foundations of Computer Science (Semester 2) 2011 Assessed Exercise Sheet 9 10% of Continuous Assessment Mark
• Extending ACOR to Solve Multi-Objective Problems Abel Garcia-Najera
• Understanding the Emergence of Modularity in Neural Systems John A. Bullinaria
• Identification and Exploitation of Linkage by Means of Alternative Splicing
• A New Approach for Incremental Modular Neural Networks in Time Series Forecasting
• 1. Introduction In recent years, increasingly sophisticated techniques
• Modelling Reaction Times John A. Bullinaria
• Networks of Artificial Neurons, Single Layer Perceptrons Neural Computation : Lecture 3
• Representation, Learning, Generalization and Damage in Neural Network Models of Reading Aloud
• 1. Introduction In recent years, increasingly sophisticated techniques
• Exploring the Baldwin Effect in Evolving Adaptable Control Systems
• Group 1 -Mech. Eng. Room G42 Exercise 1 Exercise 2 Exercise 3 Exercise 4 Exercise 5 Exercise 6 Exercise 7 947498 95 94 100 69 91 94 91
• Lecture Notes for Second Semester Foundations of Computer Science
• Extracting Semantic Representations from Word Co-occurrence Statistics: Stop-lists, Stemming and SVD