Posted by **ParRus** at Oct. 24, 2016

WEBRip | English | MP4 + PDF Guides + Excercises files | 960 x 540 | AVC ~76.2 kbps | 29.970 fps

AAC | 128 Kbps | 44.1 KHz | 2 channels | 28:38:37 | 2.78 GB

This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. An introduction to fundamental data types, algorithms, and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Specific topics covered include: union-find algorithms; basic iterable data types (stack, queues, and bags); sorting algorithms (quicksort, mergesort, heapsort) and applications; priority queues; binary search trees; red-black trees; hash tables; and symbol-table applications.

Posted by **ParRus** at May 3, 2015

English | MP4 + PDF slides | 960 x 540 | AVC ~22.2 kbps | 15 fps

AAC | 128 Kbps | 44.1 KHz | 2 channels | Subs: English (srt) | ~19 hours | 1.24 GB

AAC | 128 Kbps | 44.1 KHz | 2 channels | Subs: English (srt) | ~19 hours | 1.24 GB

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.

Posted by **ParRus** at June 6, 2013

English | MP4 | 960 x 540 | AVC ~22.1 kbps | 15 fps

AAC | 122 Kbps | 44.1 KHz | 2 channels | Subs: English (srt) | 19:04:49 | 1.52 GB

AAC | 122 Kbps | 44.1 KHz | 2 channels | Subs: English (srt) | 19:04:49 | 1.52 GB

In this course you will learn several fundamental principles of advanced algorithm design. You'll learn the greedy algorithm design paradigm, with applications to computing good network backbones (i.e., spanning trees) and good codes for data compression. You'll learn the tricky yet widely applicable dynamic programming algorithm design paradigm, with applications to routing in the Internet and sequencing genome fragments.

Posted by **house23** at April 11, 2013

MP4 | AVC 21kbps | English | 960x540 | 15fps | 19 hours | AAC stereo 114kbps | 1.24 GB

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? How come QuickSort runs so fast? What can graph algorithms tell us about the structure of the Web and social networks? Did my 3rd-grade teacher explain only a suboptimal algorithm for multiplying two numbers?

Posted by **ParRus** at Dec. 25, 2016

Stanford University with Jure Leskovec, Anand Rajaraman, Jeff Ullman

WEBRip | English | MP4 + PDF Guides | 960 x 540 | AVC ~76.7 kbps | 29.970 fps

AAC | 128 Kbps | 44.1 KHz | 2 channels | Subs: English (.srt) | 20:04:35 | 1.88 GB

We introduce the participant to modern distributed file systems and MapReduce, including what distinguishes good MapReduce algorithms from good algorithms in general. The rest of the course is devoted to algorithms for extracting models and information from large datasets. Participants will learn how Google's PageRank algorithm models importance of Web pages and some of the many extensions that have been used for a variety of purposes.

Posted by **ParRus** at June 20, 2016

WEBRip | English | MP4 + work files | 960 x 540 | AVC ~151 kbps | 29.970 fps

AAC | 128 Kbps | 44.1 KHz | 2 channels | Subs: English | ~13 hours | 1.52 GB

From Google Maps to consumer global positioning system (GPS) devices, spatial technology shapes many lives in both ordinary and extraordinary ways. Thanks to spatial computing, a hiker in Yellowstone and a taxi driver in Manhattan can know precisely where they are, discover nearby points of interest and learn how to reach their destinations. Spatial computing technology is what powers the Foursquare check-in, the maps app on your smartphone, the devices used by scientists to track endangered species, the routing directions that help you get from point A to point B, the precision agriculture technology that is revolutionizing farming, and the augmented reality devices like Google Glass that may soon mediate our interaction with the real world.

Posted by **house23** at Feb. 16, 2016

MP4 | AVC 189kbps | English | 960x540 | 30fps | 13 hours | AAC stereo 127kbps | 1.89 GB

This course teaches a calculus that enables precise quantitative predictions of large combinatorial structures. Part I covers generating functions and real asymptotics and then introduces the symbolic method in the context of applications in the analysis of algorithms and basic structures such as permutations, trees, strings, words, and mappings.

Posted by **house23** at Nov. 9, 2015

MP4 | AVC 76kbps | English | 960x540 | 29.97ps | 19h 04mins | AAC stereo 108kbps | 1.67 GB

In this course you will learn several fundamental principles of advanced algorithm design. You'll learn the greedy algorithm design paradigm, with applications to computing good network backbones (i.e., spanning trees) and good codes for data compression. You'll learn the tricky yet widely applicable dynamic programming algorithm design paradigm, with applications to routing in the Internet and sequencing genome fragments. You’ll learn what NP-completeness and the famous “P vs. NP” problem mean for the algorithm designer. Finally, we’ll study several strategies for dealing with hard (i.e., NP-complete problems), including the design and analysis of heuristics. Learn how shortest-path algorithms from the 1950s (i.e., pre-ARPANET!) govern the way that your Internet traffic gets routed today; why efficient algorithms are fundamental to modern genomics; and how to make a million bucks in prize money by “just” solving a math problem!

Posted by **house23** at Jan. 29, 2015

MP4 | AVC 161kbps | English | 960x540 | 29.97fps | 14 hours | AAC stereo 128kbps | 1.68 GB

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining.

Posted by **house23** at April 1, 2014

MP4 | AVC 243kbps | English | 960x540 | 29.97fps | 8 hours | AAC stereo 64kbps | 1.11 GB

The sequencing of the human genome a decade ago fueled a computational revolution in biology, which has arguably been an impetus for more new algorithms than any other fundamental realm of science. The newly formed links between computer science and biology affect the way we teach computational ideas to biologists, as well as how applied algorithms are taught to computer scientists. Genome sequencing is just one of hundreds of biological problems that have become inextricable from the computational methods required to solve them. In this course, we will take a look at some of the algorithmic ideas that are fundamental to an understanding of modern biology. Computational concepts like dynamic programming and graph theory will help us explore algorithms applied to a wide range of biological topics, from finding regulatory motifs to reconstructing the tree of life. Throughout the process, we will apply real bioinformatics algorithms to real genetic data.