OpenClassroom. Full courses.
Short Videos. Free for everyone. Learn the fundamentals of human-computer interaction and design thinking, with an emphasis on mobile web applications. A practical introduction to Unix and command line utilities with a focus on Linux. Introduction to fundamental techniques for designing and analyzing algorithms, including asymptotic analysis; divide-and-conquer algorithms and recurrences; greedy algorithms; data structures; dynamic programming; graph algorithms; and randomized algorithms. Database design and the use of database management systems (DBMS) for applications. Machine learning algorithms that learn feature representations from unlabeled data, including sparse coding, autoencoders, RBMs, DBNs.
Introduction to discrete probability, including probability mass functions, and standard distributions such as the Bernoulli, Binomial, Poisson distributions. Introduction to applied machine learning. This is a course created to test the website. Data Mining. Amit’s A* Pages. Mindyourdecisions.com. CSE Blog - quant, math, computer science puzzles. Green Tea Press: Free Computer Science Books.
NLP. Misc. Haptic. Programming. Algos. Data structures. Digital geometry. Networks, Crowds, and Markets: A Book by David Easley and Jon Kleinberg. In recent years there has been a growing public fascination with the complex "connectedness" of modern society.
This connectedness is found in many incarnations: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity. These are phenomena that involve networks, incentives, and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which each of our decisions can have subtle consequences for the outcomes of everyone else. Networks, Crowds, and Markets combines different scientific perspectives in its approach to understanding networks and behavior. The book is based on an inter-disciplinary course that we teach at Cornell. The book, like the course, is designed at the introductory undergraduate level with no formal prerequisites.
Artificial Intelligence: A Modern Approach. Motor cognition. The concept of motor cognition grasps the notion that cognition is embodied in action, and that the motor system participates in what is usually considered as mental processing, including those involved in social interaction.[1] The fundamental unit of the motor cognition paradigm is action, defined as the movements produced to satisfy an intention towards a specific motor goal, or in reaction to a meaningful event in the physical and social environments.
Motor cognition takes into account the preparation and production of actions, as well as the processes involved in recognizing, predicting, mimicking and understanding the behavior of other people. This paradigm has received a great deal of attention and empirical support in recents years from a variety of research domains including developmental psychology, cognitive neuroscience, and social psychology. Perception-action coupling[edit] Shared representations between other and self[edit] Motor priming[edit] Social facilitation[edit] Introduction to Computer Science Class Online (CS101)
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! NP-Complete Theory.