Introductory description

This module will run in Term 2 and will be comprised of three selected topics in the area of computational challenges associated with data analysis. The topics may change year to year.

Some examples of topics from previous academic years:
Deep Learning for Natural Language Processing, Decision Trees and Random Forests, Model Comparison and Selection, Artificial Neural Networks, Introduction to Reinforcement Learning and Modelling the Written Word: Compression and Human-Computer-Interfaces.

This module is available for students on a course where it is a listed option and as an Unusual Option to students who have completed the prerequisite modules.

Pre-requisites: ST219 Mathematical Statistics B OR ST220 Introduction to Mathematical Statistics. The module CS260 Algorithms is recommended.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Topics will vary from year to year. Example syllabi include:

1. Numerical optimization: algorithms to find the minimizer of convex function e.g. gradient descent, Newton and quasi-Newton methods. Rates of convergence and computational costs associated with these algorithms in general and / or specific settings. Discussion of relative merits of different methodology.
2. The MapReduce programming model: modern approaches to scaling-up computation via distribution and parallelization, such as the map-reduce programming model, and systems such as Hadoop and Spark.
3. Streaming algorithms: algorithms to process massive streams of data. Hashing, sketching and randomization. Probabilistic counting, counting distinct elements, count-min sketch.
4. High-dimensional regression and variable selection: methods for regression with large datasets, and methods for determining which covariates are important. Statistical and computational issues relating to large numbers of covariates and / or measurements. Ridge regression, the LASSO, and variable selection. Cross-validation. Screening.
5. Coding theory: compression and error detection. Lossless coding, entropy, Shannon's theorem. Symbol and dictionary-based approaches. Error-correcting codes, parity checks, Hamming (7,4)-code.
6. Deep Learning for Natural Language Processing, introduction to the theory and practice of deep NNs with focus on the applications in natural language processing (NLP). Neural network architectures such as convolutional NNs, recurrent NNs, attention mechanisms, transformers, sequence-to-sequence learning and (time-permitting) generative adversarial networks and variational autoencoder. key concepts of artificial NNs, such as activation functions, layers, weights and gradient descent for fitting a NN;
7. Decision Trees and Random Forests: CHAID, C4.5, C5.0, ID3 and CART algorithms, bagging, boosting, random forests, the pros and cons of such approaches. Software examples (e.g. R package CHAID and rpart).
8. Model comparison and selection, Scientific validity in the context of the data analytics workflow, Basic model-agnostic assessment of supervised/predictive models, Performance quantification of models. Predictive model validationin R/mlr and python/sklearn Julia/MLJ. Statistical formulation of the supervised learning setting,
   Bias-variance trade-off, cross-validation and re-sampling estimators, Estimators of the generalization loss and the loss‘s variance, Hypothesis testing for pairwise and portmanteau model comparison, Meta-strategies for automated model improvement, Interaction of model tuning and model validation workflows
9. Artificial Neural Networks. Artificial neural networks (NNs) are a class of learning algorithms for regression, classification, and unsupervised learning that mimic real neural networks. They are very flexible and have become hugely popular in recent years. This topic will provide an introduction to the theory and practice of artificial NNs for supervised learning, building up from simple single layer feed-forward networks to complex multi-layer 'deep' architectures. We will cover some theory such as universal approximation theorems, as well as practicalities like training and regularization. Convolutional Nns. Advanced component: recurrent NNs and unsupervised NNs.
10. Reinforcement Learning: Reinforcement Learning (RL) is one of the main subfields of machine learning, alongside supervised and unsupervised learning, that focuses on decision making under implicit feedback. As such, it is heavily employed and developed in areas such as robotics and Al engines in games like Go and Chess.
    This topic will introduce the field of RL and standard agent-environment framework, covering Bellman's equations, dynamic programming, Monte Carlo and Temporal-Difference learning. Advanced Component: eligibility traces, function approximation.
11. Modelling the Written Word: Modelling of written words, viewed as streams of symbols from a finite
    alphabet, is a rich field with an extensive literature. This topic will provide an introduction to some
    probabilistic approaches to this problem and will show how these models can be used to efficiently store written text and also to provide efficient mechanisms for entry of text into computer systems which can be used without mastering the keyboard. Advanced Component: Grammar-based language models.

Learning outcomes

By the end of the module, students should be able to:

• Demonstrate understanding of the three selected topics.
• Appreciate the computational challenges associated with data analysis and use some techniques developed to meet these challenges.
• Be able to critically appraise the use of these topics.

Indicative reading list

General texts in the correct area:
Hastie, T. and Tibshirani R. (2009)"The Elements of Statistical Learning ", Corr. 9th printing 2017 edition; Springer
Bishop, C.M. (2008) "Pattern Recognition and Machine Learning" ; Springer-Verlag New York
Other texts will be specified depending on the topics covered.

View reading list on Talis Aspire

Subject specific skills

This will depend on the topic but will be the ability to understand, evaluate and apply various complex statistical, computational and machine learning tools to a variety of datasets. Students will develop skills in the use of appropriate software.

Transferable skills

The general understanding of data from a variety of contexts. The ability to identify and find new data analysis techniques and to then learn them from suitable documentation. Be able to learn coding type software.