MSc Web Science and Big Data Analytics
Award:  Master of Science (MSc) 
Level:  Postgraduate 
Duration:  1 Year 
Full/Part Time:  Full Time only 
Fees:  UK/EU £11,090 Overseas £23,020 
Research Group:  Media Futures Research Group 
Programme Contact:  Sean Taylor 
Application Deadline: 17 June 2016
Our degree
The MSc in Web Science and Big Data Analytics is a specialist programme. It covers fundamental aspects of web related technologies and big data analytics ranging from information search and retrieval, data mining and analytics, largescale distributed and cloud computing, to ecommerce and their business economic models, and to the latest concepts of web 2.0 and social networks and the underlying networks science, with potential options in machine learning, artificial intelligence, finance, software engineering, and machine vision. It is intended for students with a general science and engineering background who wish to learn all aspects of quantitative web science and big data analytical skills. We also offer the more Research orientated MRes Web Science and Big Data Analytics.
Our Graduates
MSc Web Science's unique combination of technical skills makes graduates well equipped to proceed to scientific research or the ideal choice for the best employers in Internet related industries and the areas requiring largescale data analytical skills.
Top graduate destinations include:
 Top graduate roles include:
 Top further study destinations:

Average starting salary £31,200 (all data from Graduate Surveys, January 2013)
Our Staff
Dr Jun Wang
Dr Jun Wang
Jun Wang is Senior Lecturer in University College London and Founding Director of MSc/MRes Web Science and Big Data Analytics. His main research interests are in the areas of information retrieval, data mining and online advertising. His research has been dedicated to building an Intelligent (text and nontextual media) System that can access, retrieve, change and design the media content and its representation in such a way that it is adapted to the environment and context, and suitable for an individual person. To achieve the goal, Dr. Wang has studied statistical modelling of information retrieval, social “the wisdom of crowds” approaches for content understanding and access (collaborative filtering (recommendation)), peertopeer information retrieval and filtering, and, multimedia content analysis. Recently, he has developed an interest in “Web Economy” where he intends to unify information retrieval and economic models for Web ecosystems.
Dr Shi Zhou
Dr Shi Zhou
Shi received his BSc and MSc in Electronic Engineering at Zhejiang University, China and his PhD in Telecommunications at Queen Mary, University of London in 2004. Since then he has been a Lecturer (Assistant Professor) at UCL. He was awarded a prestigious Royal Academy of Engineering/EPSRC Research Fellowship from 2007 – 2012.
Shi is a member of the Media Futures research group and the Networks research group of the Department of Computer Science. He supervises PhD students at the UCL Centre for Security and Crime Science (SECReT) and the UCL Doctoral Training Centre in Financial Computing. He is also a founding member of the UCL Academic Centre of Excellence in Cyber Security Research (ACECSR).
Shi is a Senior Member of IEEE and a committee member of the Internet Specialist (IS) group of the British Computer Society (BCS).
Dr Emine Yilmaz
Dr Emine Yilmaz
Emine is a lecturer (assistant professor) at University College London, Department of Computer Science. She also works as a research consultant for Microsoft Research, Cambridge and serves as one of the organizers of CSML, Centre for Computational Statistics and Machine Learning at UCL. Emine is one of the recipients of the Google Faculty Research Award in 2014.
Emine's research interests lie in the areas of information retrieval, web science, and applications of machine learning, probability and statistics. For more information about her recent publications, please visit her publications page.
Prof Ingemar Cox
Prof Ingemar Cox
Dr Sebastian Riedel
Dr Sebastian Riedel
Our modules
The MSc Web Science Programme consists of 8 taught modules and a Dissertation. Of the taught modules, 4 are core modules and 4 are elective modules.
4 core modules include the following:
COMPGI15  Information Retrieval & Data Mining
Code  COMPGI15 (Also taught as: COMPM052) 

Year  MSc 
Prerequisites  N/a 
Term  2 
Taught By  Jun Wang (50%), Emine Yilmaz (50%) 
Aims  The course is aimed at an entry level study of information retrieval and data mining techniques. It is about how to find relevant information and subsequently extract meaningful patterns out of it. While the basic theories and mathematical models of information retrieval and data mining are covered, the course is primarily focused on practical algorithms of textual document indexing, relevance ranking, web usage mining, text analytics, as well as their performance evaluations. Practical retrieval and data mining applications such as web search engines, personalisation and recommender systems, business intelligence, and fraud detection will also be covered. 
Learning Outcomes  Students are expected to master both the theoretical and practical aspects of information retrieval and data mining. At the end of the course student are expected to understand 1. The common algorithms and techniques for information retrieval (document indexing and retrieval, query processing, etc). 2. The quantitative evaluation methods for the IR systems and data mining techniques. 3. The popular probabilistic retrieval methods and ranking principles. 4. The techniques and algorithms existing in practical retrieval and data mining systems such as those in web search engines and recommender systems. 5. The challenges and existing techniques for the emerging topics of MapReduce, portfolio retrieval and online advertising. 
Content:
Overview of the fields
Study some basic concepts of information retrieval and data mining, such as the concept of relevance, association rules, and knowledge discovery. Understand the conceptual models of an information retrieval and knowledge discovery system.
Indexing
Introduce various indexing techniques for textual information items, such as inverted indices, tokenization, stemming and stop words.
Retrieval Methods
Study popular retrieval models: 1 Boolean, 2. Vector space, 3 Binary independence, 4 Language modelling. Probability ranking principle. Other commonlyused techniques include relevance feedback, pseudo relevance feedback, and query expansion.
Evaluation of Retrieval Performance
Measurements
Average precision, NDCG, etc. "Cranfield paradigm" and TREC conferences.
Personalisation and Usage Mining
Study basic techniques for collaborative filtering and recommender systems, such as the memorybased approaches, probabilistic latent semantic analysis (PLSA), personalized web search through clickthrough data.
Data Mining
Study basic techniques, algorithms, and systems of data mining and analytics, including frequent pattern and correlation and association analysis, anomaly detection, and clickthrough modelling.
Emerging Areas
Peertopeer information retrieval and MapReduce; Online (web) Advertising; Learning to Rank; Portfolio retrieval and Risk Management.
Method of Instruction:
Lecture presentations, Practical exercises
Assessment:
The course has the following assessment components:
 Written Examination (2.5 hours, 60%)
 Coursework (40%)
To pass this course, students must:
 Obtain an overall pass mark of 50% for all sections combined.
Resources:
Introduction to Information Retrieval, Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Cambridge University Press. 2008.
Introduction to Data Mining, PangNing Tan, Michael Steinbach and Vipin Kumar, AddisonWesley, 2006
Gigabytes (2nd Ed.) Ian H. Witten, Alistair Moffat and Timothy C. Bell. (1999), Morgan Kaufmann, San Francisco,
California.
Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer (2006).
course website
COMPGW01  Complex Networks and Web
Code  COMPGW01 (Also taught as: COMPM042) 

Year  MSc 
Prerequisites  Normally offered only to students in computer science related programmes because programming skills are required for the coursework project. 
Term  1 
Taught By  Shi Zhou (100%) 
Aims  This module introduces the fundamental concepts, principles and methods in the interdisciplinary academic field of network science, with a particular focus on the Internet, the World Wide Web and online social media networks. Topics covered include graphic structures of networks, mathematical models of networks, the Internet topology, structure of the Web, community structures, epidemic spreading, PageRank, temporal networks and spatial networks. 
Learning Outcomes  On successful completion of this module the students will be able to:

Content
Network science
Complex networks
Network graphic metrics
Random networks
Smallworld networks
Scalefree networks
Network mathematical models
Network structural constraints
Network centrality measures
Temporal networks
Spatial networks
Network visualisation
Communication and information networks
Internet core structure – evolution and modelling
Structure of the Web – PageRank and document networks
Online social media networks  Twitter, Facebook, Amazon, …
Network functions and behaviours
“Rich gets richer” phenomenon
Link, neighbourhood and community
Cascades and epidemics
Network structure balance
Sentimental, temporal and spatial analysis of social media networks
Method of Delivery
A Moodle webpage is created for the course. All course materials, such as lecture notes and online resources will be shared. By using the Moodle, students will also be able to discuss ideas and questions with the lecturer and other students.
In the second half of the term, there will be a weekly onehour lab/tutorial session, where the lecturer and/or a teaching assistant will discuss questions with students.
Assessment
The module has the following assessment components:
 Unseen written examination (2.5 hour, 70%)
 Course project (30%)
To pass this module, students must:
 Obtain an overall pass mark of 50% for all components combined.
(The Course Project consists of an individual project on network data analysis (programming is usually required), and a project report (3000 words), including literature survey, which is due by the end of the Winter Holidays.)
Resources
D. Easley and J. Kleinberg. Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge University Press, 2010.
M. E. J. Newman. Networks: An Introduction, Oxford University Press, 2010.
S. N. Dorogovtsev. Lectures on Complex Networks, Oxford University Press, 2010.
Other books for interest:
D. J. Watts. Small Worlds: The Dynamics of Networks between Order and Randomness, Princeton University Press, 1999
M. Dodge and R. Kitchin. Atlas of Cyberspace, Pearson Education, 2001.
S. N. Dorogovtsev and J. F. F. Mendes. Evolution of Networks: From Biological Nets to the Internet and WWW, Oxford University Press, 2003.
M. Mitchell. Complexity: A Guided Tour, Oxford University Press, 2009.
COMPGW02  Web Economics
Code  COMPGW02 (Also taught as: COMPM041) 

Year  MSc 
Prerequisites  Normally offered only to students in computer science related programmes because basic programming skills are required. 
Term  2 
Taught By  Emine Yilmaz (50%) Jun Wang (50%) 
Aims  The course is intended to provide an introduction of the computing systems and their economics for the production, distribution, and consumption of (digital) goods and services over the Internet and web. While the basic economic principles are covered to understand the business aspects of webbased services, the course is primarily focused on the computational and statistical methods for implementing, improving and optimizing the internetbased businesses, including algorithmic mechanism design, online auctions, user behavior targeting, yield management, dynamic pricing, cloudsourcing, social media mining and attention economics. Practical applications such as Google’s online advertising, Ebay’s online auction, and Amazon’s cloud computing will also be covered and discussed. 
Learning Outcomes  The students are expected to master both the theoretical and practical aspects of web economics. More specifically, the student will:

Content
System design
 Web basics: HTTP, HTML5 referrer, Link and Clickthrough analysis, etc
 Basic Economic Principles and Economic analysis:
 Micro vs. Macro economics
 Basic elements of Supply and Demand
 Equilibrium
 Incentives: Game theory, and Auction theory
 Business Models in the Internet:
 auction and bidding (the Ebay Model, swoopo, and b2c and b2b auctions (alibaba)
 Subscription (Compulsory license, dropbox premier model, spotify, apple icloud, pay per use).
 Online retailing (Amazon, Apple Apps).
 digital goods & bundling
 Computational advertising
 Vickrey auction and the second price auction
 Searchbased advertising, Contextual advertising and Behaviour targeting, Demandside platform and Realtime bidding, Ad exchange and futures and options
 Digital Right Management, Spam/fraud control and Internet radio
 Computing as a service/utility
 Social media mining
Management and optimization
 Dynamical pricing models (airtickets) and Yield management and scheduling (online advertising)
 Search engine optimization
People
 Attention economics and Personalization and Long tail
 Prediction market and its accuracy
 Human computing and Social computing systems
 Crowdsourcing and Amazon Mechanical Turk (MTurk) and Collective intelligence
 System design (ESP game, reCAPTCHA etc)
 Bittorrent and Peertopeer file sharing
Method of Delivery
Lectures. A website or/and moodle webpage will be created for the course and the course materials such as lecture notes, sample codes, will be shared. By using moodle, students will also be able to discuss relevant ideas and have questions answered by the lecturer.
Assessment
The module has the following assessment components:
 Written examination (2.5 hours, 70%)
 Coursework (30%)
To pass this module, students must:
 Obtain an overall pass mark of 50% for all components combined.
Resources
Noam Nisan (Editor), Tim Roughgarden (Editor), Eva Tardos (Editor), Vijay V. Vazirani (Editor), Algorithmic Game Theory, Cambridge University, 2007.
David Easley and Jon Kleinberg, Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge University Press, 2010
R. Preston McAfee, Introduction to Economic Analysis www.mcafee.cc/Introecon/IEA.pdf
Nir Vulkan, The Economics of eCommerce, Princeton University Press, 2003
Carl Shapiro, Hal R. Varian, Information rules: a strategic guide to the network economy, 1999
COMPGI19  Statistical Natural Language Processing
Code  COMPGI19 (also taught as COMPM083) 

Year  MSc 
Prerequisites  N/A 
Term  1 
Taught By  Sebastian Riedel (100%) 
Aims  The course introduced the basics of statistical natural language processing (NLP) including both linguistics concepts such as morphology and syntax and machine learning techniques relevant for NLP. 
Learning Outcomes  Students successfully completing the module should understand:

Content
NLP is domaincentred fields, as opposed to technique centred fields such as ML, and as such there is no "theory of NLP" which can be taught in a cumulative techniquecentred way. Instead this course will focus on one or two NLP endtoend "pipelines" (such as Machine Translation and Machine Reading). Through these applications the participants will learn about language itself, relevant linguistic concepts, and Machine Learning techniques. For the latter an emphasis will be on structured prediction, a branch of ML that is particularly relevant to NLP.
Topics will include (but are not restricted to) machine translation, sequence tagging, constituent and dependency parsing, information extraction, semantics.
The course has a strong applied character, with coursework to be programmed, and lab classes to teach students to write software that processes language.
Indicative contents:
 Introduction
 Machine Translation 1
 Machine Translation 2
 Document Classification and Clustering
 Tagging
 Syntactic Parsing 1
 Syntactic Parsing 2
 Coreference
 Information Extraction
 Semantic Parsing
Mode of Instruction
Lectures and lab classes, with occasional guest lectures by leading researchers in NLP.
Coursework problems will focus on basic components in an NLP pipeline, such as a document classifier, partofspeech tagger and syntactic parser.
Assessment
The course has the following assessment component:
 Coursework (100%)
Individual projects related to particular foundations, steps and techniques in the NLP pipeline. There will be 23 assignments, consisting of software to be written and presented, and a writeup.
To pass this module students must:
 Obtain an overall pass mark of 50% for all sections combined
Resources
Daniel Jurafsky and James H. Martin (2008) Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition. 2nd Edition. Prentice Hall.
4 elective modules must be chosen from the following options:
COMPGI01  Supervised Learning
Code  COMPGI01 (Also taught as: COMPM055 Supervised Learning) 

Year  MSc 
Prerequisites  Basic mathematics, Calculus, Probability and statistics, Linear algebra 
Term  1 
Taught By  Mark Herbster (50%) Massi Pontil (50%) 
Aims  This module covers supervised approaches to machine learning. It starts by reviewing fundamentals of statistical decision theory and probabilistic pattern recognition followed by an indepth introduction to various supervised learning algorithms such as Perceptron, Backpropagation algorithm, Decision trees, instancebased learning, support vector machines. Algorithmicindependent principles such as inductive bias, side information, approximation and estimation errors. Assessment of algorithms by jackknife and bootstrap error estimation, improvement of algorithms by voting methods such as boosting. Introduction to statistical learning theory, hypothesis classes, PAC learning model, VCdimension, growth functions, empirical risk minimization, structural risk minimization. 
Learning Outcomes  Gain indepth familiarity with various classical and contemporary supervised learning algorithms, understand the underlying limitations and principles that govern learning algorithms and ways of assessing and improving their performance, understand the underlying fundamentals of statistical learning theory, the complexity of learning and its relationship to generalization ability. 
Content:
Overview and Introduction to Bayes Decision Theory
Machine Intelligence and Applications; Pattern Recognition concepts
Classification, Regression, Feature Selection; Supervised Learning; Class conditional probability distributions; Examples of classifiers; Bayes optimal classifier and error; Learning classification approaches
Linear machines
General and Linear Discriminants; Decision regions; Single layer neural network;
Linear separability, general position, number of dichotomies; General gradient descent; Perceptron learning algorithm; Mean square criterion and WidrowHoff learning algorithm
MultiLayer Perceptrons
Introduction to Neural Networks, TwoLayers; Universal approximators
Backpropagation learning, online, offline; Error surface, important parameters
Learning decision trees
Inference model, general domains, symbolic; Decision trees, consistency; Learning trees from training examples;Entropy, mutual information; ID3 algorithm criterion; C4.5 algorithm; Continuous test nodes, confidence; Pruning; Learning with incomplete data
Instancebased Learning
Nearest neighbor classification; kNearest neighbor
Nearest Neighbor error probability, proof; Simplification, Editing; Example: Document retrieval; Casebased reasoning;Example: learning graphical structures
Machine learning concepts and limitations
Fundamental algorithmicindependent concepts; Hypothesis class, Target class
Inductive bias, Occam's razor; Empirical risk; Limitations of inference machines; Approximation and estimation errors; Tradeoff
Machine learning assessment and Improvement
Statistical Model Selection; Structural Risk Minimization; Practical methods for risk assessment based on resampling, Jackknife, Bootstrap; Improving accuracy of general algorithms, Bagging, Boosting
Learning Theory
Formal model of the learnable; Sample complexity; Learning in zeroBayes and realizable case; Growth function, VCdimension, VCdimension of Vector space of functions, proof Empirical Risk Minimization over finite classes, sample complexity, proof Empirical Risk Minimization over infinite classes, risk upper bound, proof Lower bound on sample complexity
Support Vector Machines
Margin of a classifier; Dual Perceptron algorithm; Learning nonlinear hypotheses with perceptron; Kernel functions, implicit nonlinear feature space Theory: zeroBayes, realizable infinite hypothesis class, finite covering, marginbased bounds on risk; Maximal Margin classifier; Learning support vector machines as a dualoptimization problem
Method of Instruction:
Lecture presentations with associated class problems
Assessment:
The course has the following assessment components:
 Written Examination (2.5 hours, 75%)
 Coursework Section (25%)
To pass this course, students must:
 Obtain an overall pass mark of 50% for all sections combined.
For full details of coursework see the course web page.
Resources:
Text Book 1: The Elements of Statistical Learning: Data Mining, Inference and Prediction, Hastie.T., Tibshirani.R., and Friedman.J., Springer [2001]
Reference Book 1: Pattern Classification, Duda.R.O., Hart.P.E., and Stork.D.G., John Wiley and Sons (2001)
Reference Book 2: Pattern Recognition and Machine Learning, Bishop, Christopher M., Springer (2006)
Reference Book 3: An Introduction to Support Vector Machines, ShaweTaylor J. and Cristianini N., Cambridge University Press (2000)
Reference Book 4: Kernel Methods for Pattern Analysis, ShaweTaylor.J, and Cristianini N., Cambridge University Press (2004)
COMPGV10  Computer Graphics
Code  COMPGV10 (Also taught as: COMP3080 Computer Graphics) 

Year  MSc 
Prerequisites  
Term  1 
Taught By  Anthony Steed (100%) 
Aims  To introduce the fundamental concepts of 3D computer graphics and give the students all the knowledge needed for creating an image of a virtual world from first principles. 
Learning Outcomes  The students will be able to define a virtual world and create images of it. They will know how to write a basic ray tracer, and use a graphics library such as OpenGl (or equivalent). 
Content:
Introduction
The painter's method.
Creating an image using ray tracing
Ray casting using a simple camera.
Local illumination.
Global illumination with recursive ray tracing.
Specifying a general camera
World / image coordinates.
Creation of an arbitrary camera.
Ray tracing with an arbitrary camera.
Constructing a scene
Polyhedra.
Scene hierarchy.
Transformations of objects / rays.
Other modelling techniques.
Acceleration Techniques
Bounding volumes.
Space subdivision.
From ray tracing to projecting polygons
Graphics pipeline.
Transforming the polygons to image space.
Sutherland Hodgman clipping.
Weiler Atherton clipping.
Clipping.
Polygon rasterization/Visible surface determination
Scan conversion.
Zbuffer.
Interpolated shading.
Texture mapping.
OpenGL.
Back face culling.
Culling.
Shadows
Shadow volumes.
Shadow buffer.
Shadow mapping.
Soft shadows.
The nature of light
Transport theory, Radiance, luminance, radiosity.
The radiance equation.
Radiosity method
Classical radiosity
Substructuring.
Progressive refinement.
Parametric surfaces
Bezier Curves.
BSplines Curves.
Method of Instruction:
Lecture presentations, and labclasses.
Assessment:
The course has the following assessment components:
 Written Examination (2.5 hours, 75%)
 Coursework Section (25%)
To pass this module, students must:
 Obtain an overall pass mark of 50% for all components combined.
The examination rubric is:
Answer THREE questions out of FIVE. All questions carry equal marks.
Resources:
Computer Graphics And Virtual Environments  From Realism to RealTime. Mel Slater, Yiorgos Chrysanthou, Anthony Steed, ISBN 0201624206, AddisonWesley, 2002.
COMPGI14  Machine Vision
Code  COMPGI14 (Also taught as: COMPM054 Machine Vision) 

Year  MSc 
Prerequisites  Successful completion of an appropriate Computer Science, Mathematics, or other Physical Science or Engineering undergraduate programme with sufficient mathematical and programming content, plus some familiarity with digital imaging and digital image processing. 
Term  1 
Taught By  Gabriel Brostow(100%) 
Aims  The course addresses algorithms for automated computer vision. It focuses on building mathematical models of images and objects and using these to perform inference. Students will learn how to use these models to automatically find, segment and track objects in scenes, perform face recognition and build threedimensional models from images. 
Learning Outcomes  To be able to understand and apply a series of probabilistic models of images and objects in machine vision systems. To understand the principles behind face recognition, segmentation, image parsing, superresolution, object recognition, tracking and 3D model building. 
Content:
Twodimensional visual geometry: 2d transformation family. The homography. Estimating 2d transformations. Image panoramas.
Three dimensional image geometry: The projective camera. Camera calibration. Recovering pose to a plane.
More than one camera: The fundamental and essential matrices. Sparse stereo methods. Rectification. Building 3D models. Shape from sillhouette.
Vision at a single pixel: background subtraction and color segmentations problems. Parametric, nonparametric and semiparametric techniques. Fitting models with hidden variables.
Connecting pixels: Dynamic programming for stereo vision. Markov random fields. MCMC methods. Graph cuts.
Texture: Texture synthesis, superresolution and denoising, image inpainting. The epitome of an image.
Dense Object Recognition: Modelling covariances of pixel regions. Factor analysis and principle components analysis.
Sparse Object Recognition: Bag of words, latent dirilecht allocation, probabilistic latent semantic analysis.
Face Recognition: Probabilistic approaches to identity recognition. Face recognition in disparate viewing conditions.
Shape Analysis: Point distribution models, active shape models, active appearance models.
Tracking: The Kalman filter, the Condensation algorithm.
Method of Instruction:
Lectures, practical lab classes.
Assessment:
The course has the following assessment components:
 Written Examination (2.5 hours, 80%)
 Coursework Section (2 pieces, 20%)
To pass this course, students must:
 Obtain an overall pass mark of 50% for all sections combined.
The examination rubric is:
Answer 3 questions
Resources:
Prince, S. Computer Vision: Models, Learning and Inference http://www.computervisionmodels.com/
COMPGC25  Interaction Design
Code  COMPGC25 (also taught as COMP3012)  

Year  MSc  
Prerequisites  Successful completion of years 1 and 2 of the BSc/MEng Computer Science programme or the BSc Information Management programme  
Term  2  
Taught by 
 
Aims  The module covers advanced topics in interaction design, focusing on the design of mobile and ubiquitous computing technologies. A central theme is how to design technologies to meet people's needs.  
Learning Outcomes 

Content:
The module is separated into three related streams:
Methods (Ten hours)
This series of lectures will introduce students to core interaction design methods, including approaches to conducting user research and designing, prototyping and evaluating user centred systems and technologies.
Application (Ten hours)
These more informal lectures will give students an opportunity to reflect on how to put interaction design methods into practice and to discuss ideas and issues with each other and with the teaching faculty. They will link closely to the coursework
Topics (Ten hours)
This series of lectures will introduce students to work on ubiquitous computing systems technologies that go "beyond the desktop", such as multitouch surfaces, ambient devices, mobile devices and situated displays. A key focus will be on approaches to understanding the domains where these technologies are used, prototyping and evaluation approaches.
Method of Instruction:
Lecture presentations with associated practical activities.
Assessment:
The course has the following assessment components:
 Written Examination (2 hours, 50%);
 Coursework (50%).
To pass this course, students must:
 Obtain an overall pass mark of 50% for all components combined.
The coursework is due in the first week of term 3.
COMPGI09  Applied Machine Learning
Code  COMPGI09 

Year  MSc 
Prerequisites  This course is for students following the MSc in Intelligent Systems programme who have completed or are completing the usual core and optional courses. 
Term  2 
Taught By  David Barber (100%) 
Aims  Applied Machine Learning aims to cover some of the issues that may arise in the practical application of machine learning in realworld problems. In addition, the course will cover some of the mathematics and techniques behind basic data analysis methods for both static and timeseries data. 
Learning Outcomes  The ability to: assess the effectiveness of solutions presented and to question them in an intelligent way; synthesise solutions to general openended problems covering material from the whole programme, tempered with information on commercial reality obtained from this course. 
Content:
Multivariate optimisation methods including line search, conjugate gradients and Newton's method, stochastic gradient descent, distributed optimisation.
Neural Nets and deep learning, fast nearest neighbour methods, large scale linear learning.
PCA, Canonical Correlation Analysis, matrix factorisation methods.
Gaussian Mixture Models Gaussian Process Regression/Classification
HMMs, AR models.
Method of Instruction:
Lecture presentations with associated class problems.
Assessment:
The course has the following assessment components:
 Written Examination (2.5 hours, 50%)
 Coursework Section. The coursework is based on assessed practical challenges hosted by Kaggle (50%).
To pass this course, students must:
 Obtain an overall pass mark of 50% for all sections combined
 Obtain a minimum mark of 50% in each component.
Resources:
To be notified as the course progresses, according to the business themes covered.
COMPGC18  Entrepreneurship: Theory and Practice
Code  COMPGC18 (Also taught as: COMP7008) 

Year  MSc 
Prerequisites  None 
Term  2 
Taught By  Philip Treleaven (CS) & David Chapman (MS&I) 
Aims  To provide students with the theory and practice necessary to launch a new business venture making maximum use of eCommerce strategies and software tools for entrepreneurs 
Learning Outcomes  First hand experience of the selection and deployment of tools, techniques and theories for the identification, validation and structuring of a new business venture. 
This is UCL’s principal Entrepreneurship course for students seeking to develop and test a new business idea. Over the past ten years we have taught entrepreneurship to around 3000 students resulting in the launch of a number of innovative businesses. The course covers: the new business lifecycle (selecting and testing a moneymaking idea, preparing a business plan, raising finance, the Exit), aspects of new business operation (registering a company, setting up your office, understanding financial statements), and exploiting new eCommerce tools and techniques (doing business electronically, company web sites, online business software and services).
Content:
 Invention and innovation – finding & qualifying new opportunities. Business Model Generation.
 Confirming customer needs & testing market demand. Customer development.
 Lean Startups: what is your minimum viable product? The value of prototyping.
 Delivery channels and customer relationships. Business Plan & Preparing a Pitch.
 Financial Forecasting, Costing and Pricing. Management accounts. Cashflow and Profit & loss.
 Developing sustainable competitive advantage. Intellectual Property Rights.
 Corporate form & structure. Founder dilemmas  team, equity, remuneration etc. Developing your brand.
 Defining and testing critical business model uncertainties. Measuring progress  common startup metrics.
 Sources of Funding. Presenting to VCs.
 Class presentations. Conclusions and nextsteps.
Method of Instruction:
10 x 2hour lectures;
10 x 1hour New Venture Clinics;
10 x 1hour Guest entrepreneurship lectures.
Assessment:
The course has the following assessment components:
 Group coursework portfolio (60%);
 Individual coursework (40%).
To pass this module, students must:
 Obtain an overall pass mark of 50% for all components combined.
Resources:
Blank, S. & Dorf, B. 2012. The Startup owner’s manual: The stepbystep guide for building a great company. K&S Ranch inc.
Mullins, J. 2006. The New Business Road Test. FT Prentice Hall
Osterwalder, A. et.al. 2014. Value Proposition Design. Wiley.
Ries, E. 2011. The Lean Startup: How Constant Innovation Creates Radically Successful Businesses. Portfolio Penguin
COMPGZ03  Distributed Systems and Security
Code  COMPGZ03 (Also taught as: COMPM030) 

Year  4 
Prerequisites  good understanding of objectoriented programming and design and networking protocols 
Term  1 
Taught By  Brad Karp (100%) 
Aims  The first half of the class explores the design and implementation of distributed systems in casestudy fashion: students read classic and recent research papers describing ambitious distributed systems. In lecture, students critically discuss the principles that cause these systems to function correctly, the exten to which these systems solve the problem articulated by the authors and the extent to which the problem and solution chosen by the quthors are relevant in practice. The second half of the class explores computer system security, again, largely in casestudy fashion. 
Learning Outcomes  Correctness under concurrency is a central challenge in distributed systems and one that can only fully be understood through experience of building such systems (and encountering subtle bugs n them). To give students experience of this sort, the module includes one significant programming coursework in C, in which the students implement a simple distributed system that must provide an ordering guarantee. Further written coursework helps students solidify their understanding of the security material in the class. 
Content:
Course introduction; OS concepts
Design: Worse is Better; Concurrent IO; RPC & Transparency
Ivy: Distributed Shared Memory
Bayou: Weak Connectivity and Update Conflicts; GFS: The Google File System
RouteBricks: ClusterBased IP Router; Introduction to Security; User Authentification
Cryptographic Primitives I; Cryptographic Primitives II;
Secure Sockets Layer (SSL); Reasoning Formally about Authentification : TAOS
Software Vulnerabilities and Expoits; Preventing Exploits
Containing Buggy Code: Softwarebased Fault Isolation; OKWS: Approximating Least Privilege in a RealWorld Web Server
Method of Instruction:
Lectures, casestudies
Assessment:
The course has the following assessment components:
 Written Examination (2.5 hours, 70%)
 Coursework Section (30%)
To pass this course, students must:
 Obtain an overall pass mark of 50% for all sections combined.
Resources:
PSYCGI11 Understanding Usability & Use
PSYCGI11 Understanding Usability & Use
Module code:  PSYCGI11(Add to my personalised list) 
Title:  Understanding Usability and Use 
Credit value:  15 
Division:  Division of Psychology and Language Sciences 
Module organiser:  Ann Blandford 
Organiser's location:  MPEB, room 8.14 
Organiser's email:  a.blandford@ucl.ac.uk 
Available for students in Year(s):  
Module prerequisites:  Module is compulsory for students on MSc in HCIE. 
Module outline:  This module will equip students with the practical skills needed for the assessment of interactive systems. This will include analytical approached (based on theories of cognition and interaction) and empirical approaches (gathering and analysing data from users). Analytical approaches will include inspection techniques, based on heuristics (or checklists), and theoretically grounded methods. In U3, the focus is on qualitative approaches to evaluating systems in their context of use, including interviews and observations. Students will develop their critical thinking skills, in relation to both the systems being evaluated and the choice of technique to apply in the evaluation. 
Module aims:  Students will become familiar with a range of data gathering and analysis methods that are relevant to the concerns of HumanComputer Interaction. They will be aware of the scope and applicability of those methods, and be able to select and apply appropriate methods according to requirements. They will be able to present the findings of evaluations through written reports. 
Module objectives:  This module will equip students with the practical skills needed for the assessment of interactive systems. This will include analytical approaches (based on theories of cognition and interaction) and empirical approaches (based on the gathering and analysis of data from users). It will also include theoretical understanding of the strengths and limitations of evaluation methods for interactive systems design. Analytical approaches will include inspection techniques and more explicitly theoretically grounded methods. Empirical approaches will focus on qualitative techniques. The course will cover the design of studies, and the gathering and analysis of data. 
Key skills provided by module:  
Module timetable:  cmis.adcom.ucl.ac.uk/timetabling/moduleTimet.do 
Module assessment:  One piece of coursework (2,5003000 words) 100.00%. 
Notes:  
Taking this module as an option?:  
Link to virtual learning environment(registered students only)  moodle.ucl.ac.uk/course/view.php; 
Last updated:  20140317 13:59:56 by ucacrbe 
BUCI029H7 Cloud Computing (Birkbeck)
BUCI029H7 Cloud Computing (Birkbeck)
Module Description
Module Name, Abbreviated Name, Code
Cloud Computing, CC, BUCI029H7
Credits, Level
15 credits, level 7
Lecturer
Dell Zhang
Online Material
Module Outline
Students in this module will learn to understand the emerging area of cloud computing and how it relates to traditional models of computing, and gain competence in MapReduce as a programming model for distributed processing of big data.
Aims
This module aims to introduce backend cloud computing techniques for processing "big data" (terabytes/petabytes) and developing scalable systems (with up to millions of users). We focus mostly on MapReduce, which is presently the most accessible and practical means of computing for "Webscale" problems, but will discuss other techniques as well.
Syllabus
 Introduction to Cloud Computing
 Cloud Computing Technologies and Types
 Big Data
 MapReduce and Hadoop
 Running Hadoop in the Cloud (Practical Lab Class)
 Developing MapReduce Programs
 Data Management in the Cloud
 Information Retrieval in the Cloud
 Link Analysis in the Cloud
 Beyond MapReduce
 Selected Case Studies
 Advanced Topics in Cloud Computing
Prerequisites
Good knowledge of Java programming would be necessary. Students who did not have much experience in this area before joining their respective MSc programmes should have already taken the ISD (BUCI021S7) module.
Timetable
All dates and timetables are now listed in the programme booklets of the individual programmes.
Coursework
A couple of programming assignments.
Assessment
Coursework (20%). Examination (80%).
Recommended Reading
 Jothy Rosenberg and Arthur Mateos, The Cloud at Your Service, Manning, 2010.
 Jimmy Lin and Chris Dyer, DataIntensive Text Processing with MapReduce, Morgan and Claypool, 2010.
 Extensive use is made of other relevant book chapters and research papers that are distributed or provided online.
If you have a question about the MSc Information and Web Technologies that is not covered here or on the Birkbeck FAQ , please contact Liam Simmonds.
Programme Administrator: Liam Simmonds
Admissions Tutor: Andrea Cali
Programme Director: Nigel Martin
More details about our modules can be found here