Stochastic Processes

Exercises, exams and solutions for a second year maths course.

Adding Camtasia video files to Blackboard

This short video shows how to add a video produced using Camtasia Studio to a Blackboard course

Adding pictures, SmartArt and drawings to a thesis in MS Word 2010 (PC) Manual

Adding illustrations can make it much easier to make a point in a thesis. Download this manual on how in insert pictures, insert SmartArt (a selection of pre-defined diagram types) and draw your own diagram with shapes in MS Word 2010. The guide also shows you how to create a Table of Figures in the thesis.

Adding pictures, SmartArt and drawings to a thesis in MS Word 2011 (Mac) Manual

Adding illustrations can make it much easier to make a point in a thesis. Download this manual on how in insert pictures, insert SmartArt (a selection of pre-defined diagram types) and draw your own diagram with shapes in MS Word 2011. The guide also shows you how to create a Table of Figures in the thesis.

Adding an EndNote reference list in the Reference section of a thesis in Word 2010 (PC)

This video shows how to get your list of EndNote references in to the Reference section of the University template. EndNote wants to place the list at the very end of the document, but in the University's thesis structure the References are followed by the Bibliography. This video shows how to deal with this issue.

Adding pictures, SmartArt and drawings to a thesis in MS Word 2013 (PC) Manual

Adding illustrations can make it much easier to make a point in a thesis. Download this manual on how in insert pictures, insert SmartArt (a selection of pre-defined diagram types) and draw your own diagram with shapes in MS Word 2013. The guide also shows you how to create a Table of Figures in the thesis.

The Age of Social Machines

Many of the most successful and important systems that impact our lives combine humans, data, and algorithms at Web Scale. These social machines are amalgamations of human and machine intelligence. This seminar will provide an update on SOCIAM, a five year EPSRC Programme Grant that seeks to gain a better understanding of social machines; how they are observed and constituted, how they can be designed and their fate determined. We will review how social machines can be of value to society, organisations and individuals. We will consider the challenges they present to our various disciplines.

Hierarchical Prediction Machines and Big Data Analytics

An emerging consensus in cognitive science views the biological brain as a hierarchically-organized predictive processing system. This is a system in which higher-order regions are continuously attempting to predict the activity of lower-order regions at a variety of (increasingly abstract) spatial and temporal scales. The brain is thus revealed as a hierarchical prediction machine that is constantly engaged in the effort to predict the flow of information originating from the sensory surfaces. Such a view seems to afford a great deal of explanatory leverage when it comes to a broad swathe of seemingly disparate psychological phenomena (e.g., learning, memory, perception, action, emotion, planning, reason, imagination, and conscious experience). In the most positive case, the predictive processing story seems to provide our first glimpse at what a unified (computationally-tractable and neurobiological plausible) account of human psychology might look like. This obviously marks out one reason why such models should be the focus of current empirical and theoretical attention. Another reason, however, is rooted in the potential of such models to advance the current state-of-the-art in machine intelligence and machine learning. Interestingly, the vision of the brain as a hierarchical prediction machine is one that establishes contact with work that goes under the heading of 'deep learning'. Deep learning systems thus often attempt to make use of predictive processing schemes and (increasingly abstract) generative models as a means of supporting the analysis of large data sets. But are such computational systems sufficient (by themselves) to provide a route to general human-level analytic capabilities? I will argue that they are not and that closer attention to a broader range of forces and factors (many of which are not confined to the neural realm) may be required to understand what it is that gives human cognition its distinctive (and largely unique) flavour. The vision that emerges is one of 'homomimetic deep learning systems', systems that situate a hierarchically-organized predictive processing core within a larger nexus of developmental, behavioural, symbolic, technological and social influences. Relative to that vision, I suggest that we should see the Web as a form of 'cognitive ecology', one that is as much involved with the transformation of machine intelligence as it is with the progressive reshaping of our own cognitive capabilities.