English

This course first outlines the importance of strategically-oriented stakeholder engagement, due to the growing set of claims addressed to the private sector, then focuses on the tools and techniques available to design and implement effective “mega-marketing” or “pre-market” strategies, which in complex institutional settings are more and more often critical in determining the outcome of traditional market strategies. Throughout the course students develop two complementary sets of skills underpinning the development of corporate political strategies: analytical skills, required to understand the needs, expectations, and “rules of the game” associated to the interaction with non-market stakeholders; and design and implementation skills, required to harness the tools and techniques which can be used to enact effective non-market strategies. The course relies on lectures by the instructor and guest speakers, complemented by a mix of class discussions, case studies, and role-playing exercises to provide the framework needed to connect the many facets of non-market strategies on the one hand, and to practice with the levers managers can use when trying to shape or to position their firms in the non-market environment on the other.

This course introduces the fundamentals of natural language processing (NLP), i.e. computational models of language and their applications to text. This course combines machine learning (ML), including fundamental formalisms and algorithms, with a strong hands-on experience. The course covers topics including the NLP tasks of language modeling, text classification, semantics, information extraction, parsing, pragmatics, machine translation, summarization, and answering questions. Methods covered include text classification, structured prediction, representation and deep learning, conditional random fields, and beam search. Students learn efficient implementations and the relationship between NLP tasks. This course also explores the themes of discriminative and generative learning, and various ways of obtaining supervision for training statistical NLP models. Students apply the skills and concepts learned in this course to other fields including data science, political science research, and gene sequencing.

The provides an introductory overview of public health issues from both legal and policy perspectives. The interdisciplinary approach offers students a foundational understanding on the intersectionality involved in public health by investigating the power and motivation behind the governance of healthcare through legislative action and judicial decisions. The course begins with an introduction to the key institutions and players that manage and regulate public health. The course then transitions to understanding the types of texts that offer authority in healthcare. Students are asked to explore a variety of policy papers, legislative drafts, information notes as well as relevant case law (in several jurisdictions) that together govern the behavior and outcomes of both scientific and healthcare communities, policy and lawmakers, and undoubtedly the everyday human. The course provides students starter tools in policy analysis and engagement with legal texts. Students are expected to have completed all assigned readings to ensure fruitful discussion an interactive experience participating in the in-class activities.

This course explores human population issues and problems in our contemporary world from a geographical perspective. It focuses on theories and definitions in population geography, patterns and trends in fertility, mortality and migration of human populations in different parts of the world, how population issues are shaped by economic, political, social, cultural, and environmental changes. Topics covered include high level of fertility in poor countries, aging population in advanced economies, migration, and the HIV-AIDS pandemic and major health problems in different parts of the world.

This course covers methods for solving scientific problems using computers. It provides a training in the computational modeling of scientific problems and their representation using computer graphics. It also provides a grounding in object-oriented programming through the practical application of the Python programming language. Students carry out extensive practical and project work. The course also covers key concepts of object oriented programming; analysis of problem statements to produce simple object oriented designs; object oriented programming using Python; use of packages for scientific programming and visualization in Python; and writing simple graphical applications in Python to visualize experimental results.

An introduction to the ecophysiological responses of plants and animals to environmental and anthropogenic stress, with an emphasis on the effects of changes in global climate and land use. The course focuses on biological functions as they are affected by interactions with their physical, chemical and biotic environments.