London School of Economics

The primary focus of this course is on the core machine learning techniques in the context of high-dimensional or large datasets (i.e. big data). The first part of the course covers elementary and important statistical methods including nearest neighbors, linear regression, logistic regression, regularization, cross-validation, and variable selection. The second part of the course deals with more advanced machine learning methods including regression and classification trees, random forests, bagging, boosting, deep neural networks, k-means clustering and hierarchical clustering. The course will also introduce causal inference motivated by analogy between double machine learning and two-stage least squares. All the topics are delivered using illustrative real data examples. Students also gain hands-on experience using R or Python (programming languages and software environments for data analysis, computing and visualization).

This course covers the following: 1. study of 20th-century British literature (prose, poetry, and drama) in its socio-political context; study of individual authors (in weekly lectures) 2. study of major cultural themes running through the century, e.g., literature of war; imperialism; feminism; modernism; postmodernism; political writing, 3. several trips to theatre productions during the year, 4. extensive use of archive recordings of authors, and video.

This course discusses some central empirical and theoretical questions in the field. It begins by examining classic comparative debates about the relationship between the development of states and nations on the one hand, and the rise of capitalism and democracy on the other. The course examines the impact that social cleavages have on parties, elections and other political institutions in a number of different countries. It also examines the strength and political impact of both labor movements and other important social movements. Additionally, the course examines why similar countries can develop very different social and economic policies. In addition students examine some of the founding writings of Marx and Weber and critically assess the use of political concepts. Throughout the course students consider some of the main theoretical approaches that are used in the study of political sociology.

This course examines, from a philosophical perspective, the ways in which recent developments in genetics and neuroscience challenge our conceptions of what we are — and what we could become.

The course provides an introduction into monetary and financial history from the 18th century to the present day. It examines the main developments in international monetary architecture and the global financial system since the Glorious Revolution. The course introduces students to major concepts of money and finance (financial development, financial integration, monetary policy, banking crises etc.) and to provide a long run perspective to the current policy debate.

The course covers key statistical methods and data analytic techniques most relevant to finance. Hands-on experience in analyzing financial data in the “R” environment is an essential part of the course. The course includes a selection of the following topics: obtaining financial data, low- and high-frequency financial time series, ARCH-type models for low-frequency volatilities and their simple alternatives, Markowitz portfolio theory and the Capital Asset Pricing Model, concepts and  practices in machine learning as applied in financial forecasting, Value at Risk. The course covers classification techniques using random forests and simple trading strategies if time permits.

Throughout the 18th century, thousands of young British men and women embarked on extensive journeys to continental Europe – an activity known as the Grand Tour.  This course explores who these people were, where they went, and the reasons for their expeditions.  For some, the Tour was the final stage of formal education; to others an opportunity for sexual adventures and pleasure-seeking. The course discusses the practical challenges of 18th-century travel, the political, religious, and cultural contexts of the Tour, as well as the key places to visit and the reasons for their popularity.  It also considers what the Tourists brought back with them: from physical artefacts for public and private collections, to new ways of seeing and understanding the world. The course introduces students to the actual writings of the Tourists, showing how they experienced international travel and shaped the modern tourist industry.

Data science and machine learning are exciting new areas that combine scientific inquiry, statistical knowledge, substantive expertise, and computer programming. One of the main challenges for businesses and policy makers when using big data is to find people with the appropriate skills. Good data science requires experts that combine substantive knowledge with data analytical skills, which makes it a prime area for social scientists with an interest in quantitative methods. This course extends the foundation of probability and statistics with an introduction to the most important concepts in applied machine learning, with social science examples. It covers the main analytical methods from this field with hands-on applications using example datasets, so that students gain experience with and confidence in using the methods covered. 

The course introduces students to basic principles of artificial intelligence (AI) systems. By AI, we refer to machines (or computers) that mimic cognitive functions that humans associate with the human mind, such as learning and problem solving. The course takes a practical approach, explaining the main principles and methods used in the design of AI systems. The course provides an introduction to main principles of deep learning, covering topics of neural nets as universal approximators, design of neural network architectures, backpropagation and optimization methods for training neural networks, and some special deep neural network architectures commonly used for solving AI tasks such as image classification, sequence modelling, natural language processing and generative models. If time allows, this course also provides an introduction to reinforcement learning problem formulation. Students gain practical knowledge to learn and evaluate deep learning and reinforcement learning algorithms (if time allows) using Python and open-source software libraries.

The first part of the course focuses on the foundations of public economics. The course introduces classical theories concerning equity, efficiency, and the rationales for government intervention in the economy. Students discuss market failure in the context of public goods and externalities, including environmental policy. They also discuss problems of public choice and political economics, and the implications of recent research in behavioral economics for policymaking. Students also study modern empirical methods that are used to evaluate the causal effects of public policies. The second part of the fall term considers social insurance policies, including unemployment insurance, disability insurance, retirement pensions, and public health insurance. Students study the economic rationale for government intervention in social insurance and the optimal type and extent of interventions, and they relate this to empirical evidence on the causal effects of changes in social insurance policies. In the spring term, the course is devoted to tax and transfer programs. The course begins by examining the incentive effects of taxes and transfers on labor supply, and then goes on to consider migration, tax avoidance, and tax evasion. As inequality is a key input to tax and transfer policy, we critically examine the measurement of and trends over time in income and wealth inequality in various countries. We look at the effect of taxation on economic efficiency and explore the optimal taxation of commodities and income. Finally, we consider questions of tax administration and apply them in a developing country context.