Computational Neuroscience About the Course This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information. Course Syllabus Topics covered include: 1. Recommended Background Familiarity with basic concepts in linear algebra, calculus, and probability theory. In-course Textbooks
ranzato-06.pdf 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.
Deep Learning Tutorials — DeepLearning 0.1 documentation Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. See these course notes for a brief introduction to Machine Learning for AI and an introduction to Deep Learning algorithms. Deep Learning is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text. For more about deep learning algorithms, see for example: The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano. The algorithm tutorials have some prerequisites. The code is available on the Deep Learning Tutorial repositories. The purely supervised learning algorithms are meant to be read in order: Building towards including the mcRBM model, we have a new tutorial on sampling from energy models: LSTM network for sentiment analysis:
Learning How to Learn This course gives you easy access to the invaluable learning techniques used by experts in art, music, literature, math, science, sports, and many other disciplines. We’ll learn about how the brain uses two very different learning modes and how it encapsulates (“chunks”) information. We’ll also cover illusions of learning, memory techniques, dealing with procrastination, and best practices shown by research to be most effective in helping you master tough subjects. Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life. 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.
Applied Cryptography and Encryption When does the course begin? This class is self paced. You can begin whenever you like and then follow your own pace. It’s a good idea to set goals for yourself to make sure you stick with the course. How long will the course be available? This class will always be available! How do I know if this course is for me? Take a look at the “Class Summary,” “What Should I Know,” and “What Will I Learn” sections above. Can I skip individual videos? Yes! How much does this cost? It’s completely free! What are the rules on collaboration? Collaboration is a great way to learn. Why are there so many questions? Udacity classes are a little different from traditional courses. What should I do while I’m watching the videos? Learn actively!
Creative Problem Solving About the Course This course will help you understand the role of creativity, innovation, and problem solving in your own life and across disciplines. It will challenge you to move outside of your existing comfort zone and to recognize the value of that exploration. What makes an idea creative, anyway? This course will help you understand the importance of diverse ideas, and to convey that understanding to others. The principal learning activity in the course is a series of "differents" where you will be challenged to identify and change your own cultural, habitual, and normal patterns of behavior. Course Syllabus Introduction: including creativity as an area of study, course methods, and doing something different. Recommended Background No background required, all learners are welcome. In-course Textbooks As a student enrolled in this course, you will have free access to selected chapters and content for the duration of the course. Suggested Readings Johnson, Steven. Lehrer, Jonah.
Introduction to Mathematical Thinking About the Course NOTE: For the Fall 2015 session, the course website will go live at 10:00 AM US-PST on Saturday September 19, two days before the course begins, so you have time to familiarize yourself with the website structure, watch some short introductory videos, and look at some preliminary material. The goal of the course is to help you develop a valuable mental ability – a powerful way of thinking that our ancestors have developed over three thousand years. Mathematical thinking is not the same as doing mathematics – at least not as mathematics is typically presented in our school system. The course is offered in two versions. Course Syllabus Instructor’s welcome and introduction 1. 2. 3. 4. 5. 6. 7. 8. 9. Recommended Background High school mathematics. Suggested Readings There is one reading assignment at the start, providing some motivational background. There is a supplemental reading unit describing elementary set theory for students who are not familiar with the material.
Logic: Language and Information 1 About the Course Information is everywhere: in our words and our world, our thoughts and our theories, our devices and our databases. Logic is the study of that information: the features it has, how it’s represented, and how we can manipulate it. Learning logic helps you formulate and answer many different questions about information: Does this hypothesis clash with the evidence we have or is it consistent with the evidence? If you take this subject, you will learn how to use the core tools in logic: the idea of a formal language, which gives us a way to talk about logical structure; and we'll introduce and explain the central logical concepts such as consistency and validity; models; and proofs. Course Syllabus Week 1. Week 2. Weeks 3–5. Electronic Engineering — simplifying digital circuitsPhilosophy — vagueness and borderline casesComputer Science — databases, resolution and propositional PrologLinguistics — meaning: implication vs implicature Recommended Background Suggested Readings
Critical Thinking Course Summary This MOOC is an introduction in Critical Thinking, with an emphasis on using reason in our daily communication. Its main topics cover the structure and analysis of arguments, the study of inductive reasoning as basis for scientific knowledge and as key ingredient in how we understand reality. What do I learn? After taking this course you will have the tools to analyze the truth of all kinds of statements, from opinion articles to court verdicts and investment proposals. What do I need to know? No prior knowledge is needed for this course, participants should only come equipped with natural curiosity and a respect for the truth. Course Structure Chapter & Topic Chapter 1: Introduction to Critical Thinking - what is critical thinking, why study it, a short history 1.1: Road Map 1.2: Is Man the Reasoning Animal? Chapter 2: Arguments ABC - understanding arguments as building blocks of reason; argument structure and analysis 2.1: Simple Argument. Chapter 3: Deductive vs.