English

This course provides a study of the methodology for the analysis of equilibrium and efficiency in exchange economies, production economies, and environments with external effects.

This course offers a study of basic Machine Learning techniques including when to use Machine Learning to solve real problems, which techniques are appropriate, and how to apply them in a practical way. It examines classification and various techniques: prediction, non-supervised, and reinforcement-based ones. This course also discusses relational learning and methodological aspects of machine learning.  

Pre-requisites- Programming and Statistics 

This course helps liberal arts students to develop their skills of analysis and understanding. Students do so both individually and in teams. Students focus on topical or real-life events and explore them using a wide range of methods and approaches linked to the arts, humanities and social sciences, including media analysis, statistical analysis, textual analysis and visual analysis. This course is built around a stake holder meeting, which explores a specific topic (potentially based on a recent real-world example). It involves the introduction to what a stake holder meeting is; introduction to case studies and modes of analysis; introduction to group work and team theory; introduction to presentation of argument and analysis in essay form.

This course examines the political economy of global monetary and financial relations. It is structured around such questions as: What is the global financial system and what purposes does it serve? What are the choices of monetary and financial policies open to national governments, and what determines governments’ different policy choices? How do governments and markets interact in the arena of global finance? How do private actors influence the governance of international finance? When and why are efforts to regulate global markets successful, and what are the distributional consequences of such efforts? What are the political causes and effects of global financial crises? In seeking answers to these questions, this course focuses on empirical and theoretical political economy models of money and finance. 

This introductory course analyzes the elements have shaped identity and culture in Mexico throughout history, as well as the values that various artistic expressions developed over time.  The course aims to:

1. Discover the historical development of the configuration of identities in Mexican culture, through the concepts of art, crafts, artist, craftsman, cutlure, folklore, acculturation and transculturation.
2. Distinguish artisan production and culinary culture and its connection with local and regional identities.
3. Distinguish particular processes of aesthetic production in visual culture, architecture and Mexican design in their historical context.
4. Explore the evolution of photography, film, and the media in Mexico, as well as its historical contribution to the development of gender, race and class identities. 

This course introduces students to the study of the relationship between language and society. The course includes topics like language variation and change, language and gender, multilingualism and language contact, and language policy. Aspects like the distinction between language and dialect are covered next to how language attitudes shape our communicative behavior and the way we perceive speakers. The course has both theoretical and empirical content and includes many case studies and practical exercises from languages and regions around the world. This course is recommended for linguistics majors and is an important asset for anyone who seeks to understand how language affects how we relate to each other in society. Prerequisite: LING1000.

Machine Learning is the science of how we can build abstractions of the world from data and use them to solve problems in a data-driven way. This course allows students to both understand the principles upon which Machine Learning methods are based and learn the practical skills required to apply Machine Learning to solve real problems.

We need to regulate our thoughts, feelings and behavior in order to achieve our goals and succeed in life. In this course, students consider the processes involved in self-regulation, the role of emotions in self-regulation, and the relationship between self-regulation and mental, physical, and social wellbeing. Students also discuss the factors that lead people to fail at self-regulation, and the interventions and techniques people can use to improve their regulation ability and thus achieve their goals. Students learn about controlling emotions, combating procrastination, forming good habits, and overcoming smoking, overeating, and overspending. Students gain theoretical and practical insights into how people successfully pursue their goals, and apply these insights to their own lives.

This course provides a comprehensive introduction to the field of international management, focusing particularly on the cultural, social, and organizational challenges that arise when conducting business across national borders. The course develops foundational competencies needed to understand, analyze, and navigate cultural diversity in professional contexts. Through theoretical frameworks, case studies, and practical exercises, students learn to interpret how cultural values shape managerial behavior, organizational structures, communication styles, and decision‑making processes around the world. The course provides the analytical tools necessary to understand why managerial practices differ across countries and how to adapt their own approaches when operating in international environments.

This course covers various applications of cognitive science to practical and theoretical problems in psychology. Lectures and research projects offered in the course are aimed at developing students’ appreciation and understanding of the research methodologies and real-world applications of cognitive science. Topics covered include connectionist architectures (neural networks), influences of biological cycles, drugs, and hormones on cognitive performance, the cognitive psychology of decision making, memory in the forensic arena, face recognition and reconstruction, clinical cognition, and evolutionary cognitive psychology among others. DP requirements: Completion of all coursework, as well as completion of 90 minutes in the Student Research Participation Programme (SRPP) or equivalent and attendance of at least 5 tutorials. Assessment: Coursework: counts a total of 50%. This coursework is broken down into weekly tests (25%), and a group field project (25%). Examination: the two-hour examination in June counts 50% towards the final mark Course entry requirements: Students must have passed PSY2015F and PSY2014S.