Imperial College London

This course studies the basic chemical engineering techniques in nuclear processing including ion exchange, liquid-liquid extraction, radiation, and reactor coolant chemistry. Course applications include fuel production, fuel processing, and waste management. This course presents information on chemical and chemical engineering aspects of nuclear power, including fuel manufacture, radiation chemistry, reactor coolant chemistry, fuel reprocessing, and waste disposal.

This course explores the basic principles used in the design of modern operating systems. It examines device and I/O management functions in operating systems as part of a uniform device abstraction as well as disk organization and file system structure. Students are able to give the rationale for virtual memory abstractions in operating systems and understand the main principles and techniques used to implement processes and threads as well as the different algorithms for process scheduling. In addition, they understand the main mechanisms used for inter-process communication and understand the main problems related to concurrency and the different synchronization mechanisms available. Student gain the ability to evaluate security risks in operating systems and understand the role operating systems can and should play in establishing security.

In this course, students analyze the behavior of reinforced concrete subjected to flexure, shear, and axial loading. Students explore the concept of reinforced concrete design at serviceability and ultimate limit states. Students undertake the design of reinforced concrete beams, slabs, and columns in accordance to Eurocode 2.

The course focuses on heterogeneous and multi-phase reactors. Through understanding the underlying physics of the different reactor types, the student is equipped to carry out reactor design tasks for conventional and novel reactors in a systematic way. Particular focus is on teaching a generally applicable problem solution approach, which is of relevance to professional practice.

Students will gain an awareness and basic understanding of some of the non-engineering aspects that will be encountered by professional engineers working in a professional environment. The main topics include: engineering finance; project management;ethics and professional responsibillity; and intellectual property. After this class, students will understand the range of responsibilities of a professional engineer. We will examine how organisations and projects are financed and investment decisions made, as well as studying the difficulties of managing projects and provide some tools for planning and managing projects. We will look at the importance of ethical standards and be able to discuss some example ethical situatIons.

In this course, students work individually and in groups to learn brainstorming techniques and design methodologies, improve drafting and drawing skills, improve communication and presentation skills, and expand creativity. Students learn to think more creatively about engineering design and discover the modern design landscape. The course culminates with a product design challenge project and essay, as well as an exhibition of each student’s work throughout the term. Students also keep a daily drawing journal to experiment with different drawing styles and techniques as well as keep track of individual progress. Assessment is by coursework.

This class will provide students the techniques and tools to devise and develop Natural Language Processing (NLP) components and applications. The course will cover the foundations, building blocks and applications of NLP, with an emphasis on the necessary linguistic intuitions as well as a broad coverage of statistical and deep learning models that can be used for language tasks. NLP is an important topic in Artificial Intelligence with a wide range of applications, from sentiment analysis to machine translation. Modern NLP is primarily based on statistical methods and machine learning algorithms, where linguistic information is provided by instances of uses of language. For most NLP tasks, state of the art approaches are based on neural models, which will be at the core of this module. However, significant attention will be given to the linguistic principles that underpin the field. More specifically, students will: Gain familiarity with important linguistic concepts involved in language understanding and generation, from morphological analysis to pragmatics Gain familiarity with, devise, implement and apply relevant pre-processing steps for natural language processing components and applications Critically compare statistical and deep learning approaches for natural language processing Map various well established techniques in machine learning to specific problems in natural language processing Build, evaluate, critically analyze and improve models using ex-isting machine learning algorithms and frameworks (such as TensorFlow) for a range of natural language processing tasks, including: classification, structured prediction, sequence to se-quence labeling and generation Devise, implement and evaluate classifiers for a range of natu-ral language processing tasks. Learning outcomes After the course, students should be able to: (ILO1) Identify and automatically pre-process texts that can be useful for language processing tasks (ILO2) Devise and evaluate solutions for a range of natural lan-guage components using existing algorithms, techniques and frameworks, including part-of-speech tagging, language mod-eling, parsing and semantic role labeling (ILO3) Devise, implement and evaluate algorithms for single and multi-class classification problems (ILO4) Apply existing statistical and deep learning techniques to language applications such as machine translation. ILO1 and IL3 will be assessed mainly through the coursework, while ILO2 and ILO4 will be assessed mainly via exam. Module syllabus 1 Introduction to NLP (language challenges, applications, clas-sical vs statistical vs deep learning-based) 2 Basic concepts in Linguistics (including morphology, syntax, semantics, pragmatics) 3 Pre-processing techniques, word meaning (TF-IDF, distribu-tional models, word2vec, glove, etc) 4 Lab/tutorial session on pre-processing and word meaning 5-6 Classification tasks with simple classification models (Naïve Bayes, perceptron): SPAM detection, part-of-speech tagging, word sense disambiguation 7 Classification tasks with CNN models 8 Lab/tutorial session on classification 9 Coursework specification and discussion 10 N-gram language models 11 Neural language models (RNNs, LSTMs, GRUs) 12 Lab/tutorial session on language models 13 Structured prediction - POS tagging with HMM 14 Structured prediction - POS tagging with neural models (RNN) 15 Syntax and parsing 16 Lab/tutorial session on POS tagging 17 Rules-based and probabilistic parsing 18 Neural models for parsing 19 Semantic role labeling 20 Lab/tutorial session on parsing 21-23 Sequence to sequence modelling - machine translation (SMT, NMT, attention) 24 Lab/tutorial session on sequence to sequence modelling 25 Guest lecture on advanced NLP topics 26 Guest lecture on advanced NLP topics 27 Revision lecture 28 Revision lecture

This course provides an introduction to problem solving in civil and environmental engineering using MATLAB. Through hands-on experience students learn concepts of computer-assisted engineering computation, graphical presentation, and programming.