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J. Alex Halderman University of Michigan Start: 3 September 2012 (5 weeks long)
Information, Technology, and Design
Computer Science: Systems, Security, Networking In this course, you'll learn what every citizen should know about the security risks--and future potential — of electronic voting and Internet voting. Computer technology has transformed how we participate in democracy. The way we cast our votes, the way our votes are counted, and the way we choose who will lead are increasingly controlled by invisible computer software. Most U.S. states have adopted electronic voting, and countries around the world are starting to collect votes over the Internet. However, computerized voting raises startling security risks that are only beginning to be understood outside the research lab, from voting machine viruses that can silently change votes to the possibility that hackers in foreign countries could steal an election. This course will provide the technical background and public policy foundation that 21st century citizens need to understand the electronic voting debate. You'll learn how electronic voting and Internet voting technologies work, why they're being introduced, and what problems they aim to solve. You'll also learn about the computer- and Internet-security risks these systems face and the serious vulnerabilities that recent research has demonstrated. We'll cover widely used safeguards, checks, and balances — and why they are often inadequate. Finally, we'll see how computer technology has the potential to improve election security, if it's applied intelligently. Along the way, you'll hear stories from the lab and from the trenches on a journey that leads from Mumbai jail cells to the halls of Washington, D.C. You'll come away from this course understanding why you can be confident your own vote will count — or why you should reasonably be skeptical.
Ezekiel J. Emanuel, MD, PhD Penn University of Pennsylvania Start: TBA (8 weeks long)
Workload: 8-10 hours/week
Health and Society & Medical Ethics This course will explore the complex challenges of allocating scare medical resources at both the micro and macro level. Students will learn the theories behind allocation and use modern examples to explore how society makes the difficult decisions that arise when there is not enough to go around. You have one liver but three patients are awaiting a liver transplant. Who should get the liver? What criteria should be used to select the recipient? Is it fair to give it to an alcoholic? These are some of the questions that arise in the context of rationing and allocating scarce health care resources among particular individuals. These are called micro-allocation decisions. There are also macro-allocation decisions that focus on how health care systems distribute resources across populations. Using the cases of organs for transplantation, the rationing for vaccines in a flu pandemic, and oncology drug shortages, the course will critically examine alternative theories for allocating scarce resources among individuals. Using both the need to establish priorities for global health aid and to define an essential benefit package for health insurance, the course will critically examine diverse theories for macro-allocation from cost-effectiveness analysis to age-based rationing to accountability for reasonableness. There are no prerequisites or required knowledge to take this course.
Antonio Rangel Caltech Start: 7 January 2013 (10 weeks long)
Workload: 8-10 hours/ week
Economics & Finance Quantitative and model-based introduction to basic ideas in economics, and applications to a wide range of real world problems. The impact of economic forces in our lives is sizable and pervasive. For this reason, it is impossible to understand the social and economic forces shaping our lives without a good understanding of basic economic principles. This course provides a quantitative and model-based introduction to such principles, and teaches how to apply them to make sense of a wide range of real world problems. Examples of applications include predicting the impact of technological changes in market prices, calculating the optimal gasoline tax, and measuring the value of new products. This is a real Caltech class. It will be taught concurrently to Caltech and on-line students. This has two implications. On the costs side: the class is challenging, highly quantitative, and will demand significant effort. On the benefit side: successful completion of the class will provide you with an in-depth understanding of basic economics, and will permanently change the way you see the world.
Katie Clark UCSF Start: January 2013 (6 weeks long)
Workload: 2-4 hours/week
Medicine, Health and Society & Medical Ethics This course covers the basics of normal nutrition for optimal health outcomes and evidence-based diets for a variety of diseases. Participants will learn the fundamentals of nutrition science and build upon these to explore emerging diet therapies, to analyze nutrition research and to plan well-balanced meals and dietary interventions for both healthy individuals and those with a number of diseases and health conditions.
Geoffrey Hinton University of Toronto Start: 17 September 2012 (8 weeks long)
Workload: 7-9 hours/week
Statistics, Data Analysis, and Scientific Computing
Computer Science: Artificial Intelligence, Robotics, Vision Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well. Neural networks use learning algorithms that are inspired by our understanding of how the brain learns, but they are evaluated by how well they work for practical applications such as speech recognition, object recognition, image retrieval and the ability to recommend products that a user will like. As computers become more powerful, Neural Networks are gradually taking over from simpler Machine Learning methods. They are already at the heart of a new generation of speech recognition devices and they are beginning to outperform earlier systems for recognizing objects in images. The course will explain the new learning procedures that are responsible for these advances, including effective new proceduresr for learning multiple layers of non-linear features, and give you the skills and understanding required to apply these procedures in many other domains.
Michael Kearns Penn University of Pennsylvania Start: September 2012 (10 weeks)
Information, Technology, and Design
Computer Science: Artificial Intelligence, Robotics, Vision Networked Life will explore recent scientific efforts to explain social, economic and technological structures -- and the way these structures interact -- on many different scales, from the behavior of individuals or small groups to that of complex networks such as the Internet and the global economy. Networked Life looks at how our world is connected -- socially, strategically and technologically -- and why it matters. The answers to the questions above are related. They have been the subject of a fascinating intersection of disciplines, including computer science, physics, psychology, sociology, mathematics, economics and finance. Researchers from these areas all strive to quantify and explain the growing complexity and connectivity of the world around us, and they have begun to develop a rich new science along the way. Networked Life will explore recent scientific efforts to explain social, economic and technological structures -- and the way these structures interact -- on many different scales, from the behavior of individuals or small groups to that of complex networks such as the Internet and the global economy. This course covers computer science topics and other material that is mathematical, but all material will be presented in a way that is accessible to an educated audience with or without a strong technical background. The majority of the course is grounded in scientific and mathematical findings of the past two decades or less (often much less). Networked Life is the flagship course of the new Market and Social Systems Engineering Program at the University of Pennsylvania.
Vicki Colvin, Daniel Mittleman Rice University Start: TBA Material Science Nanotechnology is an emerging area that engages almost every technical discipline – from chemistry to computer science – in the study and application of extremely tiny materials. This short course allows any technically savvy person to go one layer beyond the surface of this broad topic to see the real substance behind the very small. Nanotechnology is an exciting research area that spans disciplines from electrical engineering to biology. Over the last two decades the basic science of this area has launched new technologies, the first examples of which are finding their way into commercial products. This eight lecture course will provide students with a birds eye view into this fast moving area and leave students with an appreciation of the importance and foundation of super small materials and devices.
José J. Vazquez-Cognet Illinois Start: TBA (6-8 weeks)
Workload: 5-7 hours/week
Economics & Finance Introduction to the functions of individual decision-makers, both consumers and producers, within the larger economic system. Primary emphasis on the nature and functions of product markets, the theory of the firm under varying conditions of competition and monopoly, and the role of government in promoting efficiency in the economy. Most people make the incorrect assumption that economics is ONLY the study of money. My primary goal in this course is to shatter this belief. In fact, in the last 50 years economists have tackled some of the most interesting and important questions for humanity. For instance, the following are just a few examples:
• About Love and Marriage
• Why is the divorce rate so high and what should we do in order to reduce it?
• About the Environment
• Why do we have so much pollution?
• How much is an endangered species worth?
• About Crime
• Would legalizing marijuana lead to a reduction in crime?
• About Labor Markets
• Does increasing the Federal Minimum Wage put people out of work?
• Why did so many women enter the labor force during the last 40 years?
• About things you should be worrying about
• Why is a college education a smart investment? Or isn't?
• Why are the presidential candidates missing the point on college costs?
• And about many other things:
• Would school vouchers lead to improvements in public education?
• Why a draft would only damage the army?
• How to level the playing field in baseball?
• AND, why is COURSERA offering this course free of charge?
We will be addressing many of these (and many more) questions during this course. Again, my main goal will to show you the way economists think and how to use this analytical system to answer questions related not only to these and other important human issues but pretty much to anything you end up doing with your life after this class. After all, as you will quickly find out, I believe everything is economics!
Brian Caffo Johns Hopkins Bloomberg School of Public Health Start: 24 September 2012 (7 weeks)
Workload: 3-5 hours per week
Statistics, Data Analysis, and Scientific Computing Presents fundamental probability and statistical concepts used in biostatistical data analysis. Taught at an introductory level for students with junior- or senior-college level mathematical training. Statistics is a thriving discipline that provides the fundamental language of all empirical research. Biostatistics is simply the field of statistics applied in the biomedical sciences. This course puts forward key mathematical and statistical topics to help students understand biostatistics at a deeper level. After completing this course, students will have a basic level of understanding of the goals, assumptions, benefits and negatives of probability modeling in the medical sciences. This understanding will be invaluable when approaching new statistical topics and will provide students with a framework and foundation for future self learning. Topics include probability, random variables, distributions, expectations, variances, independence, conditional probabilities, likelihood and some basic inferences based on confidence intervals. Math and Logic Courses
Carol Muller Penn University of Pennsylvania Start: 23 July 2012 (7 weeks)
Humanities and Social Sciences Learn the ideas and vocabulary for listening to world music, and examine the music of six world music cultures and how they have entered into mainstream popular culture. With the click of a mouse, now more than ever we are able to access sounds made by people from all around the world. And yet, most of us don't listen to the wide diversity of music available to us, probably because it sounds so strange. This class will open up the world of music to you. We begin with a brief history of recording technology, the music industry and the place of world music in that narrative; you are introduced to keywords for talking about music cross-culturally; and then proceed to half a dozen musical cultures around the world. In each of these musical cultures, we examine the ways in which music works in those distant cultures, how it sounds, what it means, who may perform it; and then we ask ourselves where this music has traveled and entered into the Western popular culture as entertainment, political discourse, or artistic purpose.
Jennifer Campbell, Paul Gries University of Toronto Start: TBA (5 weeks)
Workload: 6-8 hours per week
Computer Science: Programming & Software Engineering Not all programs are created equal. In this course, we'll focus on writing quality code that runs correctly and efficiently. We'll design, code and validate our programs and learn how to compare programs that are addressing the same task. Most programs are used for years and are worked on by many people. Having programs that are easy to understand is essential, in the same way that a well-organized essay is far easier to follow than a disorganized one. We’ll show you an approach that helps to break down problems into smaller tasks that are easier to both solve and read. This design approach also makes it more straightforward to find and fix flaws. You'll be introduced to the tools that professional programmers use; they're called "testing" and "debugging". For most complex problems, there are many programs that solve them. Some are inherently slower than others. You'll learn how to read two programs and compare them for efficiency.
Jeffrey S. Moore Illinois Start: TBA (7 weeks)
Workload: 10-15 hours/week
Chemistry Organic chemistry course surveying introductory topics in structure and reactivity with an emphasis on elementary reaction mechanisms. This course surveys the reactions of organic compounds with an emphasis on mechanisms and a structure-based approach to understanding reactivity. Concepts and models are developed to build intuition about the reactivity of organic compounds. These concepts will prepare students for a mechanistic-based approach to learning organic reactivity. Emphasis will also be placed on developing problem-solving skills unique to organic chemistry. This course, orgchem1b, together with its prerequisite, orgchem1a are equivalent to a first-semester organic chemistry course; these courses and the subsequent courses (orgchem2a and orgchem2b) are especially suited for students in agricultural, nutritional and biological sciences, as well as premedical, predental, and preveterinary programs.
Emma Jakoi, Jennifer Carbrey Duke University Start: 23 January 2013 (12 weeks)
Workload: 6-8 hours/week
Medicine
Biology & Life Sciences In this course, students learn to recognize and to apply the basic concepts that govern integrated body function (as an intact organism) in the body's nine organ systems. The goal of this course is to provide an introduction to human physiology. The students learn to recognize and explain the basic concepts that govern each organ and organ system and their integration to maintain homeostasis, as well as some clinical aspects of failure of these systems. The organ systems covered include: nervous, muscle, cardiovascular, respiratory, endocrine, male and female reproductive, gastrointestinal, and urinary. This human physiology course is targeted to undergraduate and graduate students with an elementary background in biology. In a typical undergraduate setting, this course would fulfill requirements for students applying to professional health science programs such as medical school, nursing, physician assistant, pathologists’ assistant, physical therapy, and doctorate of physical therapy. In addition it is an ideal course in preparation for the MCAT exam.
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J. Nathan Kutz University of Washington Start: TBA Statistics, Data Analysis, and Scientific Computing Investigate the flexibility and power of project-oriented computational analysis, and enhance communication of information by creating visual representations of scientific data. Investigate the flexibility and power of project-oriented computational analysis. Practice using this technique to resolve complicated problems in a range of fields including the physical and engineering sciences, finance and economics, medical, social and biological sciences. Enhance communication of information by creating visual representations of scientific data. Take a project-oriented computational approach to solving problems in the physical and engineering sciences, finance and economics, medical, social and biological sciences. Apply advanced MATLAB routines and toolboxes to resolve problems. Review and practice graphical techniques for information presentations and learn to create visual illustrations of scientific results.
Daphne Koller Stanford University Start: 24 September 2012 (11 weeks long)
Workload: 8-10 hours/week
Computer Science: Artificial Intelligence, Robotics, Vision In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques. Uncertainty is unavoidable in real-world applications: we can almost never predict with certainty what will happen in the future, and even in the present and the past, many important aspects of the world are not observed with certainty. Probability theory gives us the basic foundation to model our beliefs about the different possible states of the world, and to update these beliefs as new evidence is obtained. These beliefs can be combined with individual preferences to help guide our actions, and even in selecting which observations to make. While probability theory has existed since the 17th century, our ability to use it effectively on large problems involving many inter-related variables is fairly recent, and is due largely to the development of a framework known as Probabilistic Graphical Models (PGMs). This framework, which spans methods such as Bayesian networks and Markov random fields, uses ideas from discrete data structures in computer science to efficiently encode and manipulate probability distributions over high-dimensional spaces, often involving hundreds or even many thousands of variables. These methods have been used in an enormous range of application domains, which include: web search, medical and fault diagnosis, image understanding, reconstruction of biological networks, speech recognition, natural language processing, decoding of messages sent over a noisy communication channel, robot navigation, and many more. The PGM framework provides an essential tool for anyone who wants to learn how to reason coherently from limited and noisy observations.
Stephen Marshak Illinois Start: TBA Workload: 8-10 hours/week
Physical & Earth Sciences Planet Earth, an overview of geology, discusses how earthquakes, volcanoes, mountain building, floods, ice ages, evolution, climate, and plate tectonics have interacted over deep time to produce a our dynamic island in space, and its unique landscapes. Earthquakes, volcanoes, mountain building, ice ages, landslides, floods, life evolution, plate motions—all of these phenomena have interacted over the vast expanses of deep time to sculpt the dynamic planet that we live on today. Planet Earth presents an overview of our home, from a geological perspective. We learn how the elements comprising the Earth formed in the guts of stars and supernovae, how Earth and its Solar System neighbors formed from a vast cloud of gas and dust over 4.5 billion years ago. And how the atmosphere, oceans, and rocks of our planet form and change. We will emphasize how plate tectonics—the grand unifying theory of geology—explains how the map of our planet's surface has changed radically over geologic time, and why as present-day geologic activity—including a variety of devastating natural disasters—occur where they do. We will also discuss the fascinating interactions and exchanges that take place among land, sea, air, and life, and how these interactions they result in the great variety of landscapes—from deserts to glaciers—that make our planet unique, and influence climate change in the past and, potentially the future. Finally, we will delve into the processes that produce the energy and mineral resources that modern society depends on, to help understand the context of the environment and sustainability challenges that we will face in the future.
Jonathan D. Moreno, PhD Penn University of Pennsylvania Start: January 2013
Workload: 8-10 hours/week
Health and Society & Medical Ethics This course will examine the ethical, legal and social issues raised by neuroscience. Topics will include the implications of new knowledge of the brain for our understanding of selfhood, for the meaning of privacy, for the distinction between therapy and enhancement, and for national security. Neuroethics might well be the most rapidly growing area within bioethics; indeed, in some respects neuroethics has grown as an independent field, with its own journals, professional society and institutional centers. This growth over the past decade is partly attributable to the growth of neuroscience itself and to the challenging philosophical and moral questions it inherently raises. A 2012 Royal Society report observes that “(a)n increasingly mechanistic understanding of the brain raises a host of ethical, legal, and social implications. This has laid the foundation for the emergent field of Neuroethics, which examines ethical issues governing the conceptual and practical developments of neuroscience. Irrespective of their validity, even the claims that modern neuroscience entails the re-examination of complex and sensitive topics like free will, consciousness, identity, and responsibility raises significant ethical issues. As such, neuroethics asks questions that extend beyond the usual umbrella of biomedical ethics.” This course will, therefore, consider the new knowledge and ways of learning about the brain from scientific and ethico-legal and social standpoints. We will examine the core themes of neuroethics, including cognitive enhancement, the nature of the self and personhood, neuroimaging and privacy, and the ways that all these themes are brought together in matters affecting national security. “Perhaps a man really dies when his brain stops, when he loses the power to take in a new idea.” --George Orwell
Mung Chiang Princeton University Start: 17 September 2012 (10 weeks long)
Workload: 8 hours/week
Economics & Finance
Information, Technology, and Design
Computer Science: Systems, Security, Networking A course driven by 20 practical questions about wireless, web, and the Internet, about how products from companies like Apple, Google, Facebook, Netflix, Amazon, Ericsson, HP, Skype and AT&T work. You pick up your iPhone while waiting in line at a coffee shop. You google a not-so-famous actor, get linked to a Wikipedia entry listing his recent movies and popular YouTube clips of several of them. You check out user reviews on Amazon and pick one, download that movie on BitTorrent or stream that in Netflix. But suddenly the WiFi logo on your phone is gone and you're on 3G. Video quality starts to degrade, but you don't know if it's the server getting crowded or the Internet is congested somewhere. In any case, it costs you $10 per Gigabyte, and you decide to stop watching the movie, and instead multitask between sending tweets and calling your friend on Skype, while songs stream from iCloud to your phone. You're happy with the call quality, but get a little irritated when you see there're no new followers on Twitter. You may wonder how they all kind of work, and why sometimes they don't. Take a look at the list of 20 questions below. Each question is selected not just for its relevance to our daily lives, but also for the core concepts in the field of networking illustrated by its answers. This course is about formulating and answering these 20 questions.
Dan Jurafsky, Christopher Manning Stanford University Start: : To Be Announced (8 weeks)
Workload: 8-10 hours/week
Computer Science: Artificial Intelligence, Robotics, Vision In this class, you will learn fundamental algorithms and mathematical models for processing natural language, and how these can be used to solve practical problems. This course covers a broad range of topics in natural language processing, including word and sentence tokenization, text classification and sentiment analysis, spelling correction, information extraction, parsing, meaning extraction, and question answering, We will also introduce the underlying theory from probability, statistics, and machine learning that are crucial for the field, and cover fundamental algorithms like n-gram language modeling, naive bayes and maxent classifiers, sequence models like Hidden Markov Models, probabilistic dependency and constituent parsing, and vector-space models of meaning. We are offering this course on Natural Language Processing free and online to students worldwide, continuing Stanford's exciting forays into large scale online instruction. Students have access to screencast lecture videos, are given quiz questions, assignments and exams, receive regular feedback on progress, and can participate in a discussion forum. Those who successfully complete the course will receive a statement of accomplishment. Taught by Professors Jurafsky and Manning, the curriculum draws from Stanford's courses in Natural Language Processing. You will need a decent internet connection for accessing course materials, but should be able to watch the videos on your smartphone.
Scott E. Page University of Michigan Start: September 2012 (10 weeks)
Workload: 4-8 hours/week
Economics & Finance
Humanities and Social Sciences In this class, you will learn how to think with models and use them to make sense of the complex world around us. We live in a complex world with diverse people, firms, and governments whose behaviors aggregate to produce novel, unexpected phenomena. We see political uprisings, market crashes, and a never ending array of social trends. How do we make sense of it? Models. Evidence shows that people who think with models consistently outperform those who don't. And, moreover people who think with lots of models outperform people who use only one.
Why do models make us better thinkers?
Models help us to better organize information - to make sense of that fire hose or hairball of data (choose your metaphor) available on the Internet. Models improve our abilities to make accurate forecasts. They help us make better decisions and adopt more effective strategies. They even can improve our ability to design institutions and procedures. In this class, I present a starter kit of models: I start with models of tipping points. I move on to cover models explain the wisdom of crowds, models that show why some countries are rich and some are poor, and models that help unpack the strategic decisions of firm and politicians. The models covered in this class provide a foundation for future social science classes, whether they be in economics, political science, business, or sociology. Mastering this material will give you a huge leg up in advanced courses. They also help you in life.
Here's how the course will work.
For each model, I present a short, easily digestible overview lecture. Then, I'll dig deeper. I'll go into the technical details of the model. Those technical lectures won't require calculus but be prepared for some algebra. For all the lectures, I'll offer some questions and we'll have quizzes and even a final exam. If you decide to do the deep dive, and take all the quizzes and the exam, you'll receive a certificate of completion. If you just decide to follow along for the introductory lectures to gain some exposure that's fine too. It's all free. And it's all here to help make you a better thinker!
Leonard E. White Duke University Start: 25 March 2013 (8 weeks)
Workload: 16-20 hours/week
Medicine, Biology & Life Sciences Explore the structure and function of the human central nervous system. Learn why knowledge of human neuroanatomy, neurophysiology, neural plasticity, and new discovery in the brain sciences matters for clinical practice. Medical Neuroscience explores the organization and physiology of the human central nervous system. This course is designed for first-year students in graduate-level health professions programs. It builds upon knowledge acquired in prior studies of cellular and molecular biology, general physiology, and gross human anatomy. The course provides students an understanding of the essential principles of neurological function, from cellular and molecular mechanisms of neural signaling and plasticity to the organization and function of sensory and motor systems. This course emphasizes the neural and vascular anatomy of the human brain and spinal cord, providing an anatomical framework for localizing lesions within the central nervous system. It also emphasizes the neurobiological foundation for understanding mental illness and disorders of human behavior. The overall goal is to equip students of the health professions for interpreting impairments of sensation, action and cognition that accompany neurological injury, disease or dysfunction. Students currently pursuing advanced studies in the brain sciences will benefit from this course by learning the fundamentals of functional neuroanatomy and how neuroscience discovery translates to clinical practice.
Andrew Ng Stanford University Start: 20 August 2012 (10 weeks)
Workload: 5-7 hours/week
Statistics, Data Analysis, and Scientific Computing
Computer Science: Artificial Intelligence, Robotics, Vision Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. What is Machine Learning? Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Jennifer Campbell, Paul Gries University of Toronto Start: 24 September 2012 (7 weeks)
Workload: 6-8 hours/week
Computer Science: Programming & Software Engineering Behind every mouse click and touch-screen tap, there is a computer program that makes things happen. This course introduces the fundamental building blocks of programming and teaches you how to write fun and useful programs using the Python language. A computer program is a set of instructions for a computer to follow, just as a recipe is a set of instructions for a chef. Laptops, kitchen appliances, MP3 players, and many other electronic devices all run computer programs. Programs have been written to manipulate sound and video, write poetry, run banking systems, predict the weather, and analyze athletic performance. This course is intended for people who have never seen a computer program. It will give you a better understanding of how computer applications work and teach you how to write your own applications. More importantly, you’ll start to learn computational thinking, which is a fundamental approach to solving real-world problems. Computer programming languages share common fundamental concepts, and this course will introduce you to those concepts using the Python programming language. By the end of this course, you will be able to write your own programs to process data from the web and create interactive text-based games.
Mitchell Green University of Virginia Start: TBA Humanities and Social Sciences An investigation of the nature and limits of self-knowledge from the viewpoints of philosophy, psychoanalysis, experimental psychology, neuroscience, aesthetics, and Buddhism. Readings are drawn from classical Western, non-Western, and contemporary sources. The Delphic Oracle is said to have had two premier injunctions: NOTHING IN EXCESS, and KNOW THYSELF. This course will be an examination of the latter injunction. Our central questions fall into two categories. First, What is it? We shall inquire into just what self-knowledge is: Is it a form of inner perception, somewhat like proprioception, by virtue of which our minds (and hearts) have internal scanners of their own states? Or should we construe self-knowledge in a way not crucially relying on a perceptual model? In that case, what other model might we use? Second, Why is it such a big deal? We shall inquire into the question why self-knowledge should be thought so important. Just what, if anything, is missing from a person lacking in self-knowledge that makes her significantly less wise, virtuous, or able than others who have this capacity? Our exploration will take us into research in Western philosophy, psychoanalysis, current experimental psychology, neuroscience, aesthetics, and Eastern philosophy as well. In aid of these investigation we will become students of our own dreams, and cultivate some meditative practices.
Jeffrey S. Moore Illinois Start: TBA (7 weeks)
Workload: 10-15 hours/week
Chemistry Organic chemistry course surveying introductory topics in structure and reactivity with an emphasis on structural fundamentals including electronic structure, conformation and stereochemistry. This course surveys structural chemistry of organic compounds with an emphasis on electronic structure, conformation and stereochemistry. Concepts and models are developed to build intuition about the stability and reactivity of organic compounds. These concepts will prepare students for a mechanistic-based approach to learning organic reactivity. Emphasis will also be placed on developing problem-solving skills unique to organic chemistry. This course, orgchem1a together with orgchem1b are equivalent to a first-semester organic chemistry course; these courses and the subsequent courses (orgchem2a and orgchem2b) are especially suited for students in agricultural, nutritional and biological sciences, as well as premedical, predental, and preveterinary programs.
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