English

This course attempts to ground the analysis of human movement by focusing on the specific historical, sociological, economic, political, and cultural impact of migration from the "migrant's point of view." Accordingly, this interdisciplinary course locates "drivers" for migration in the post-war period, trace the settlement processes, engage with the myriad challenges and developments migrants faced as new workers and citizens, before exploring the impact on succeeding generations. Through a salient ethnographic perspective of experiences, the course provides students with overarching and critical theoretical and methodological approaches to the study of migration, diaspora, and the nation-state in a globalized late modern context.

This course addresses evolutionary and comparative approaches to psychology. The course provides an understanding of major evolutionary forces and how they have shaped animal and human behavior and psychology. The course introduces key principles, concepts, and methodologies and relates them to specific topic areas such as the evolution of social behavior and the evolutionary origins of language and cognition.

This course provides students with a grounding in the foundational doctrines of European Union (EU) Law. It will focus both on the institutional and constitutional law of the EU and in particular on the processes of political and administrative decision-making, legislation, and adjudication. 

The course deals with current interdisciplinary research on the origins of language. Focus is placed on the relationship between biological and cultural evolution and physiological and neural conditions for language from the perspective of evolution. Furthermore, the course addresses animal communication, experiments on language acquisition in primates and other animals, the relationship between mind and language and different hypotheses on the origins of language and evaluation of these on the basis of empirical evidence.

Have you heard of Big Data or AI? What about Data Science? Data Science is the field of study that deals with data acquisition, data analysis, and decision making with domain knowledge. In the discipline of Data Science, data refer to either structured or unstructured data, which is commonly referred to as Big Data. Tools for analyzing Big Data in Data Science are called machine learning that is a sub-field of Statistics, and machine learning is known as a workhorse of AI. This mathematical statistics course is designed to provide a comprehensive introduction to the mathematical study of statistics (or machine learning). Without the knowledge of mathematical statistics, you cannot fully understand machine learning algorithms including Deep Learning. Topics include probability, random variables, univariate or multivariate distributions, elementary statistical inference, and limiting distributions. Emphasis is on the theoretical development and practical implementation of each topic, including definitions, theorems, proofs, computer programming, and simulations.

Prerequisites: STA1001. Introduction to Statistics (or equivalent course), STA1002. Calculus (or equivalent course)

The course introduces students to pharmacology, which can be defined as the study of the actions of drugs. The course has a strong focus on the nervous system. The basic principles of pharmacology are covered, including drug interactions with specific receptors in target tissues and pharmacokinetics. Students learn how drugs work and become familiar with pharmacological concepts and terminology. Students also consider the drug development process and the many ways in which new therapeutics are designed and developed. The effects of different classes of drugs upon the peripheral and central nervous systems and on different neurotransmitter pathways are covered. How drugs can be used to understand the function of these systems and to alleviate their malfunctioning in various diseases and afflictions is explained.

This course examines conflictual sustainability problems. It considers how to listen across significant differences of life experience and professional training and how to build network supports, identify stakeholders, develop resources and policies for implementation and evaluation to create or co-create a sustainability action plans for an applied field problem. 

Students learn about the basic concepts of electric circuits including; circuit elements, Kirchhoff’s law, basic RLC circuits, circuit theorems, Op Amp, sinusoidal steady-state analysis, frequency response and Laplace transform. Topics include Electric circuit variables, Circuit Elements, Simple Resistive Circuits, Techniques of Circuit Analysis, The Operational Amplifier, Inductance, Capacitance, and Mutual Inductance, Response of First-Order RL and RC Circuits, Natural and Step Responses of RLC Circuits, Sinusoidal Steady-State Analysis, and Sinusoidal Steady-State Power Calculations.

Prerequisites: Engineering mathematics, Differential Equations

This course explores the psychology behind entrepreneurship and innovation. The topics include the personality of entrepreneurs and exploring whether entrepreneurs are born or made; how entrepreneurs and innovation leaders make decisions about risk and manage uncertainty; what drives entrepreneurs and what "returns" they can expect (in terms of income and well-being); what success means to entrepreneurs, how individuals may lead successfully on entrepreneurial, entrepreneurs and innovation initiatives. Students also reflect on how each person can act in an entrepreneurial and innovative manner. The course examines the psychological underpinnings of the entrepreneurial process and innovative behaviors within established business. It is mindful of the diversity of entrepreneurial and innovation endeavors ranging from high-tech and digital entrepreneurs to social entrepreneurs that launch social innovations.