Lund University

The course is based on a religious studies perspective to discuss the concepts of antisemitism and islamophobia concept which refers to fears and prejudices relating to Jews, Judaism, Muslims, and Islam. By focusing on the historical, theological/ideological, political, and cultural aspects of antisemitism and islamophobia, the historical roots and the contemporary forms of these fears and prejudices are explored. The course starts by problematizing the concepts of antisemitism and islamophobia and continues by analyzing how these concepts have been used to designate "the enemy" and in processes of religious, cultural, and political "alienation". Thereafter, the historical roots and ideological contexts in which antisemitism and islamophobia arise and develop are studied. The course concludes by focusing on modern and contemporary manifestations of antisemitism and islamophobia, such as, how conspiracy theories are used to heighten hostility towards Jews and Muslims today.

The course presents an application-focused and hands-on approach to learning neural networks and reinforcement learning. It is an introduction to deep learning methods, presenting a wide range of connectionist models that represent the current state-of-the-art. Topics include the fundamentals of machine learning and the mathematical and computational prerequisites for deep learning; feed-forward neural networks, convolutional neural networks, and the recurrent connections to a feed-forward neural network; a brief history of artificial intelligence and neural networks, and reviews open research problems in deep learning and connectionism.  Entry requirements include 90 credits in statistics and a course in linear algebra.

Today’s businesses need to continuously develop their ways of organizing and leading, as well as understand how to manage innovation to secure the development and growth of the companies. This course develops a theoretical knowledge base and practical ability in innovation management. The objective is also to increase the knowledge of how considering the conditions and processes of innovation management can contribute to long-lasting advantages and an increase in company competitiveness.

This course promotes theoretical knowledge as well as applied knowledge adapted to different contexts regarding strategic management from a general manager’s perspective. The aim is further to develop an ability to analyze the strategies of an organization, the strategy formulation and implementation processes, and apply that knowledge in practical decision-making. The course begins with an overview of the different schools of strategy, the classical business policy theory, followed by the "industrial organization economics" and ends with the so-called “resourced-based view” on resources and competencies as the key basis for competitiveness. From there, the course discusses different situations and aspects with greater importance for strategic management, especially in an international business context. To highlight the differences in demands on the leadership in different kinds of organizations strategic leadership in big corporations as well as in small new ventures and public organizations with different types of objectives are discussed.

The course reviews number theory including the fundamental theorem of arithmetic, modular, and arithmetic; groups including definition, basic examples of groups, subgroups, normal subgroups, factor groups, isomorphisms, and homomorphisms, Lagrange's theorem, permutation groups, symmetric and alternating groups, finitely generated Abelian groups; rings including definition, basic examples of rings, isomorphisms and homomorphisms, ideals, factor rings, polynomial rings, factorization of polynomials as products of irreducible polynomials; and fields including characteristic, simple field extensions, finite fields.

This course introduces the concept "Design for X" (DfX), which is well established within product development. In any product development project, it is essential to ensure that the product satisfies the functions it is designed for. But many other issues are caused by, or affect the properties of the product: is the product reliable, sustainable, is it easy to assemble, and inexpensive to manufacture? In this course, the following "design for Xs" are included: design for manufacturing and assembly (DFMA), design for additive manufacturing (DFAM), robust design, design for environment (DFE), and design to cost (DtC).

The course extends the student’s Swedish vocabulary and to provide students with the opportunity to practice oral and written proficiency. The skills training also includes a presentation of the typical features of Swedish grammar and phonetics. The course also provides information on Swedish social life and institutions and glimpses of modern Swedish literature. The students extend their productive and receptive vocabulary through reading. It also includes pronunciation exercises with a particular focus on prosodic features.

This course examines environmental change as a problem of governance. It scrutinizes major governance dimensions - such as actors, institutions, and problem structures - at multiple levels and across domains. This includes the interplay of government, market, and civil society in efforts to mitigate and adapt to environmental change. The course critically approaches the changing institutional architecture of environmental governance, including the rise of alternative forms of governance beyond the state. Actors like regions, cities, international organizations, businesses, non-governmental organizations, and social movements are studied. Cases of environmental governance on various levels are contrasted and compared, together with overlaps between environmental issues and other domains like trade and security. The course allows reflecting on these cases in relation to central political scientific concepts like democracy, justice, legitimacy, and effectiveness.

The course provides an introduction to statistical analysis of text. Methods based on classic statistical approaches (including Bayesian models) and modern approaches such as deep learning (recurrent neural networks) are studied. Topics covered include preprocessing of textual data; text representation; text classification; text clustering; topic modeling; sentiment analysis; and text summarization.

The focus of the course is on Intellectual Property Right (in a global perspective) since they are considered a growing part of the value adding process in companies and at the same time are a more important mean of competition, but also related rights such as rules and regulations of marketing and competition, secrecy, and IT rights. Other issues regarding the commercialization of an invention will be discussed in the course, such as questions regarding license agreements. The course also deals with areas such as IPR-strategies for companies, how to search and utilize the information in patent databases, and how to interpret agreements relating to intellectual property.