English

This course examines histories and continuing legacies of colonialism and racialization and their interaction with legal systems and criminalization. Topics covered could include dispossession and theft of sovereignty in settler colonial nations, structural racism and its relation to law, racialized policing and profiling, mass incarceration policies and movements by Indigenous peoples and people of color to achieve social and legal justice.

This course explores generative artificial intelligence (GAI) and its applications. Students gain a comprehensive understanding of generative models, including deep learning architecture, and probabilistic models. The course covers theoretical foundations and practical implementations of generative AI algorithms. Students also engage in hands-on projects to apply generative AI methods. Topics include introduction to generative AI (overview of generative modeling, brief history of GAI, applications of GAI), probability theory and information theory, parameters estimation, latent variable models, variational inference (introduction), variational autoencoders (VAEs) - autoencoders - variational autoencoders (VAE) - conditional VAE - VQ-VAE v1, v2, generative adversarial networks (GANs) - introduction to GANs - GAN training, issues and solution - generative model evaluation, GAN variants: DCGAN, CGAN, WGAN, ProGAN and Style-GAN, GAN applications: image manipulation and editing, diffusion-based generative models - DDPM - DDIM, diffusion-based generative models - classifier guidance DMs - classifier-free guidance DMs - cascaded DMs - latent DMs, autoregressive generative models - MADE, PixelNN, language generative models - Transformer - GPT family, multi-modal generative models - DALL-E (DALL-E 2 and DALL-E 3) - stable diffusion, flow-based generative models - RealNVP, GLOW.

Prerequisite: Solid understanding of machine learning and deep learning principles - Proficiency in programming - Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch)

This course introduces the recently emerging fields of decision-making neuroscience and neuroeconomics. Topics include the core elements of the brain mechanism related to decision-making, such as dopamine function, approach and avoidance circuits, value calculation, and the dilemma of stability versus flexibility. In addition, this course covers the neuroscientific model of decision-making and how to understand and apply it to various choices in everyday life. 

This course is intended for advanced-level undergraduate students who have completed an introductory psychology course and are familiar with basic terms in neuroscience and elementary statistics. 

This course examines computation and data handling, integrating sophisticated use of existing productivity software, e.g. spreadsheets, with the development of custom software using the general-purpose Python language. Students will see examples from many domains, and be able to write code to automate the common processes of data science, such as data ingestion, format conversion, cleaning, summarization, creation and application of a predictive model.

The contemporary world is facing many challenges in promoting peace, justice and reconciliation, ranging from armed conflicts to social inequality, from environmental degradation to interfaith tensions. These complex challenges continue to afflict many parts of the globe. In this context, some fundamental questions may be asked: how do we define life?; What does it mean to live a life of integrity?; How does my life relate to just, sustainable and inclusive peace?; How do the ideas for making the world more just and peaceful shape our own lives and careers of purpose and vice versa? Seeking to explore these questions deeply, this course presents foundational theories behind peace and social justice and applies these concepts to specific fields of inquiry and practice, including: colonization, violence, oppression, racism, sexism, human trafficking, poverty, climate change and complex issues of peacebuilding, humanitarian aid and development. Various strategies and attempts to create social change for the greater good through different individual and organizational platforms are analyzed and assessed too. Throughout the course, students gain an understanding of the strengths and constraints on theory and practice in the context of the creation of a culture of “human flourishing”, particularly in post-conflict societies, and engage in a variety of topics with self-reflective approach.

This course examines skills in chemistry for application to life and medical sciences, engineering, and further study in chemistry. It covers nuclear and radiation chemistry, wave theory, atomic orbitals, spectroscopy, bonding, enthalpy and entropy, equilibrium, processes occurring in solutions, and the functional groups in carbon chemistry. Students develop experimental design, conduct and analysis skills in chemistry through experiments that ask and answer questions like how do dyes work, how do we desalinate water, how do we measure the acid content in foods, how do we get the blue in a blueprint, and how do we extract natural products from plants? 

This course buildes competency in understanding and interpreting a range of different research methods and results. Through the analysis of various research papers, students will need to use critical thinking and logical reasoning to either agree, disagree, or seek further clarification of conclusions provided in the research discussion sections.

This course explores the significant achievements of behavioral economics, focusing on theoretical and empirical evidence. Unlike traditional economics, which assumes human rationality and standard preferences to understand decision-making and behavioral changes, behavioral economics expands human decision-making models based on new insights from psychology and other fields and introduces the concept of policy design and evaluation based upon insights into human behavioral change. Students will gain a thorough understanding of the fundamental concepts of behavioral economics; be able to critically evaluate the traditional economic theories on human rationality; and be able to compare and analyze the usefulness and policy implications of behavioral economics with those of traditional economics. 

Prerequisites: Microeconomics, Basic Calculus 

This course provides the tools to analyze, as citizens as well as practitioners and workers, international affairs in the contemporary world. It explores international issues, based on research and practice, through economic, strategic, political and social lenses. National and international political issues are of particular interest. Indeed, all actors, should they be public, private, non governmental, etc., face questions related to power, financial means, culture and organization. Hence, this course begins by studying global and transversal issues to lay the foundations for examining regional and national issues. Theories of international relations are used as far as they shed light on the practice of world affairs as well as history, geography, economics, strategy and other social sciences. The course attempts to cover the most important issues, actors and geographical areas pertaining to international affairs.

What is the optimal consumption choice for a consumer given her preferences and income? When will a manager of a firm decide to expand production? What are the costs for the government when it supports the farmers with a per unit subsidy and is such a subsidy socially efficient?  Should monopolies be regulated and if so, how? When does it make sense for a firm to introduce a variable-wage payment scheme? Which attitudes to risk exist and how does this influence behavior? Such questions are treated in this course. The course is specially designed for non-economics students. Assumed previous knowledge: Students are expected to have a good command of secondary school calculus (including simple derivatives).