There's an app for that: Neuroscience Before the digital age, neuroscientists got their information in the library like the rest of us. But the explosion of neuroscience research has resulted in the publication of nearly 2 million papers — more data than any researcher can read and absorb in a lifetime. That's why a UCLA team has invented research maps. Easily accessible through an online app, the maps help neuroscientists quickly scan what is already known and plan their next study. The Aug. 8 edition of the journal Neuron describes these new tools. "Information overload is the elephant in the room that most neuroscientists pretend to ignore," said principal investigator Alcino Silva, a professor of neurobiology at the David Geffen School of Medicine at UCLA and professor of psychiatry at the Semel Institute for Neuroscience and Human Behavior at UCLA. UCLA programmer Darin Gilbert Nee also created a Web-based app to help scientists expand and interact with their field's map.
Computational Investing, Part I OverviewWhy do the prices of some companies’ stocks seem to move up and down together while others move separately? What does portfolio “diversification” really mean and how important is it? What should the price of a stock be? How can we discover and exploit the relationships between equity prices automatically? Topics We start with a tour of the mathematics and statistics that underlie equity price changes, and the relationships between different groups of equities. Important note: This is a project oriented course involving Python programming.Be sure this course is right for you! Take a look at the course syllabus here.Take a look at what other students thought of the course here.Course optionsYou can enroll in the course in several ways:Regular enrollment. Outcomes for regular and signature tracksAt the end of the course you will have created a working market simulator that you can use to test your own investing strategies.
Intro to Linear Dynamical Systems Introduction to applied linear algebra and linear dynamical systems, with applications to circuits, signal processing, communications, and control systems. Topics include: Least-squares aproximations of over-determined equations and least-norm solutions of underdetermined equations. Symmetric matrices, matrix norm and singular value decomposition. Prerequisites: Exposure to linear algebra and matrices (as in Math. 103). This Stanford course was taught on campus twice a week in 75 minute lectures for the Stanford Engineering Everywhere Initiative. AI: Neural Networks About the Course 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. This YouTube video gives examples of the kind of material that will be in the course, but the course will present this material at a much gentler rate and with more examples. Recommended Background Programming proficiency in Matlab, Octave or Python. Course Format The class will consist of lecture videos, which are between 5 and 15 minutes in length.
Direct and indirect cellular effects of aspa... [Eur J Clin Nutr. 2008 Learn to Program: Crafting Quality Code About the Course 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. For most complex problems, there are many programs that solve them. Recommended Background This course assumes “Learn To Program: The Fundamentals”, or similar background. Function definition, function call, method callTypes: bool, int, float, str, list, dict, tupleControl structures: if, for, whileFile reading and writingYou should also be familiar with the function design recipe, the Python visualizer, the IDLE debugger, and from Learn To Program: The Fundamentals. Suggested Readings Click to purchase This title is currently available in Beta. Course Format
Linear and Discrete Optimization About the Course This course serves as an introduction to linear and discrete optimization from the viewpoint of a mathematician or computer scientist. Besides learning how linear and discrete optimization can be applied, we focus on understanding methods that solve linear programs and discrete optimization problems in a mathematically rigorous way. We will answer questions like: Does a particular method work correctly? The course constitutes about half of the material on linear and discrete optimization that is taught for mathematics and computer science undergraduates at EPFL and will feature video lectures, quizzes, programming assignments, and a final exam. Course Format The class consists of lecture videos punctuated by quizzes. Logic 101 Logic 101 These lectures cover introductory sentential logic, a method used to draw inferences based off of an argument's premises. Logic is ubiquitous--individuals thinking of pursuing a career in law, computer science, mathematics, or social science must have a firm understanding of basic logic to succeed. Even someone who occasionally programs in Microsoft Excel would benefit greatly. Lectures Prerequisites Logic 101 is the ground floor--there are no prerequisites other than being willing to think through problems. Syllabus This class will cover eight topics: Simple Sentences and OperationsTruth TablesReplacement RulesRules of InferenceProofsConditional ProofsProof by ContradictionFormal Fallacies Additional information Teacher qualifications I am a PhD Candidate at the University of Rochester.
Neurological disorder MMF found to be caused by vaccines: scientific proof (NaturalNews) It is a little-known condition that can trigger persistent and debilitating symptoms similar to those associated with multiple sclerosis (MS) and fibromyalgia, but is also one that the medical profession at large is still unwilling to acknowledge. And yet emerging research continues to show that macrophagic myofasciitis, or MMF, is a very real condition brought about as a direct result of vaccines that contain aluminum adjuvants, which become lodged in muscle tissue and lead to severe neurological damage and other problems. First identified in 1998, MMF is characterized by debilitating muscle and joint pain, chronic inflammation, and incapacitating fatigue. With this loss, comes the development of serious lesions, as well as a type of autoimmune reaction in which the body is unable to properly transmit nerve impulses, and essentially ends up attacking itself. A later study published in the Ear, Nose & Throat Journal in 2007 made a similar but much more direct connection.
Model Thinking This course will consist of twenty sections. As the course proceeds, I will fill in the descriptions of the topics and put in readings. Section 1: Introduction: Why Model? In these lectures, I describe some of the reasons why a person would want to take a modeling course. These reasons fall into four broad categories: To be an intelligent citizen of the worldTo be a clearer thinkerTo understand and use dataTo better decide, strategize, and design There are two readings for this section. The Model Thinker: Prologue, Introduction and Chapter 1 Why Model? Section 2: Sorting and Peer Effects We now jump directly into some models. In this second section, I show a computational version of Schelling's Segregation Model using NetLogo. NetLogo The Schelling Model that I use can be found by clicking on the "File" tab, then going to "Models Library". The readings for this section include some brief notes on Schelling's model and then the academic papers of Granovetter and Miller and Page. Six Sigma V.S.
Statistical Mechanics: Algorithms & computations About the Course This course discusses the computational approach in modern physics in a clear yet accessible way. Individual modules contain in-depth discussions of algorithms ranging from basic enumeration methods to cutting-edge Markov-chain techniques. Emphasis will be put on applications in classical and quantum physics. The course is entirely self-contained. Recommended Background Understanding of basic calculus and linear algebra will be assumed, as well as some familiarity with College physics or chemistry. Suggested Readings The course is entirely self-contained. Much of the material is also covered in the textbook:W. Course Format This course will contain ten weeks of videotaped lectures.Mini-assignments and quizzes will be integrated into lectures, and there will be nine homework assignments with videotaped discussions of solutions. Mini-assignments and quizzes will be for self-testing only (no incidence on grades).
Introduction to Psychology Syllabus Professor Paul Bloom, Brooks and Suzanne Ragen Professor of Psychology Description What do your dreams mean? Texts Gray, Peter. Requirements Exams: There is a mid-term and a final. Reading Responses: Starting on the third week of class, you will submit a short reading response every week. Book Review: You will write one book review. Experimental participation: All Introductory Psychology students serve as subjects in experiments. Grading Reading responses: 15%Book review: 20%Midterm examination: 30%Final examination: 35% Join a Study Group Through a pilot arrangement with Open Yale Courses, OpenStudy offers tools to participate in online study groups for a selection of Open Yale Courses, including PSYC 110. View study group OpenStudy is not affiliated with Yale University.
8 Ways Tech Has Completely Rewired Our Brains Technology has altered human physiology. It makes us think differently, feel differently, even dream differently. It affects our memory, attention spans and sleep cycles. This is attributed to a scientific phenomenon known as neuroplasticity, or the brain's ability to alter its behavior based on new experiences. In this case, that's the wealth of information offered by the Internet and interactive technologies. Some cognition experts have praised the effects of tech on the brain, lauding its ability to organize our lives and free our minds for deeper thinking. Every emerging study and opinion piece is hotly disputed, yet each brings us closer to understanding how tech can fundamentally alter our minds. 1. Television impacts our psyche so thoroughly, it may even affect our dreams. Previous dream research, conducted in the early 1900s through the 1950s, has suggested a correlation between exposure to black and white television and dreaming in black and white. 2. 4. 5. 6. 8.
Gamification About the Course Gamification is the application of digital game design techniques to non-game contexts, such as business, education, and social impact challenges. Video games are the dominant entertainment form of modern times because they powerfully motivate behavior. Game mechanics can be applied outside the immersive environments of games themselves, to create engaging experiences as well as assign rewards and recognition. Over the past few years, gamification adoption has skyrocketed. Game thinking means more than dropping in badges and leaderboards to make an activity fun or addicting. Subtitles forall video lectures available in: English, Russian (provided by Digital October), Turkish (Koc University), and Ukrainian (provided by Bionic University) Course Syllabus The course is divided into 12 units. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. Recommended Background This course is designed as an introduction to gamification as a business practice. Suggested Readings Course Format Yes.