Probabilistic Graphical Models About the Course What are Probabilistic Graphical Models? 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. 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; you will also learn algorithms for using a PGM to reach conclusions about the world from limited and noisy evidence, and for making good decisions under uncertainty. Course Syllabus Topics covered include: Introduction and Overview.
Information Theory Computer Science 101 UPDATE: we're doing a live, updated MOOC of this course at stanford-online July-2014 (not this Coursera version). See here: CS101 teaches the essential ideas of Computer Science for a zero-prior-experience audience. Computers can appear very complicated, but in reality, computers work within just a few, simple patterns. CS101 demystifies and brings those patterns to life, which is useful for anyone using computers today. In CS101, students play and experiment with short bits of "computer code" to bring to life to the power and limitations of computers. Here is another video Nick created for this class.
Computer Security Natural Language Processing Game Theory Design and Analysis of Algorithms I About the Course In this course you will learn several fundamental principles of algorithm design. You'll learn the divide-and-conquer design paradigm, with applications to fast sorting, searching, and multiplication. You'll learn several blazingly fast primitives for computing on graphs, such as how to compute connectivity information and shortest paths. Finally, we'll study how allowing the computer to "flip coins" can lead to elegant and practical algorithms and data structures. Learn the answers to questions such as: How do data structures like heaps, hash tables, bloom filters, and balanced search trees actually work, anyway? Course Syllabus Week 1: Introduction. Week 2: Running time analysis of divide-and-conquer algorithms. Week 3: More on randomized algorithms and probability. Week 4: Graph primitives. Week 5: Dijkstra's shortest-path algorithm. Week 6: Further data structures. Recommended Background Suggested Readings No specific textbook is required for the course. Course Format
Computer Vision Class Central