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 Feb. 17, 2016

Dan Jurafsky, Professor of Linguistics - Stanford University

WEBRip | English | MP4 + PDF Slides | 960 x 540 | AVC ~57.4 kbps | 29.970 fps

AAC | 76 Kbps | 44.1 KHz | 1 channel | Subs: English (.srt) | 17:50:24 | 1.27 GB

AAC | 76 Kbps | 44.1 KHz | 1 channel | Subs: English (.srt) | 17:50:24 | 1.27 GB

This course covers a broad range of topics in natural language processing, including word and sentence tokenization, text classification and sentiment analysis, spelling correction, information extraction, parsing, meaning extraction, and question answering, We will also introduce the underlying theory from probability, statistics, and machine learning that are crucial for the field, and cover fundamental algorithms like n-gram language modeling, naive bayes and maxent classifiers, sequence models like Hidden Markov Models, probabilistic dependency and constituent parsing, and vector-space models of meaning.

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

MP4 | AVC 88kbps | English | 960x540 | 30fps | 16h 03mins | AAC stereo 113kbps | 3.88 GB

Commerce and research is being transformed by data-driven discovery and prediction. Skills required for data analytics at massive levels – scalable data management on and off the cloud, parallel algorithms, statistical modeling, and proficiency with a complex ecosystem of tools and platforms – span a variety of disciplines and are not easy to obtain through conventional curricula. Tour the basic techniques of data science, including both SQL and NoSQL solutions for massive data management (e.g., MapReduce and contemporaries), algorithms for data mining (e.g., clustering and association rule mining), and basic statistical modeling (e.g., linear and non-linear regression).

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 **vivid23** at Sept. 26, 2015

HDTV | 1280x720 | MKV/x264 @ 3500 Kbps | 58mn | Audio: English AAC 160 kbps, 2 channels | Subs: English | 947 MB

Without us noticing, modern life has been taken over. Algorithms run everything from search engines on the internet to satnavs and credit card data security - they even help us travel the world, find love and save lives. Professor Marcus du Sautoy demystifies the hidden world of algorithms. By showing us some of the algorithms most essential to our lives, he reveals where these 2,000-year-old problem solvers came from, how they work, what they have achieved and how they are now so advanced they can even programme themselves.

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.