E.g., 2016-07-01
E.g., 2016-07-01
E.g., 2016-07-01
Aug 2016

You've learned the basic algorithms now and are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more obvious applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision or finding dense clusters in the advertiser-search query graphs at search engines. We then proceed to linear programming with applications in optimizing budget allocation, portfolio optimization, finding the cheapest diet satisfying all requirements, call routing in telecommunications and many others. Next we discuss inherently hard problems for which no exact good solutions are known (and not likely to be found) and how to solve them approximately in a reasonable time. We finish with some applications to Big Data and Machine Learning which are heavy on algorithms right now.

Average: 5 (5 votes)
Jul 2016

World and internet is full of textual information. We search for information using textual queries, we read websites, books, e-mails. All those are strings from the point of view of computer science. To make sense of all that information and make search efficient, search engines use many string algorithms. Moreover, the emerging field of personalized medicine uses many search algorithms to find disease-causing mutations in the human genome.

Average: 6.4 (5 votes)
Jun 13th 2016

Learn about the inner workings of cryptographic primitives and protocols and how to apply this knowledge in real-world applications.

Average: 3.5 (13 votes)
Jun 6th 2016

If you have ever used a navigation service to find optimal route and estimate time to destination, you've used algorithms on graphs. Graphs arise in various real-world situations as there are road networks, computer networks and, most recently, social networks! If you're looking for the fastest time to get to work, cheapest way to connect set of computers into a network or efficient algorithm to automatically find communities and opinion leaders in Facebook, you're going to work with graphs and algorithms on graphs.

Average: 5 (2 votes)
Jun 6th 2016

In this course, we will see how evolutionary trees resolve quandaries from finding the origin of a deadly virus to locating the birthplace of modern humans. We will then use methods from computational proteomics to test whether we can reconstruct Tyrannosaurus rex proteins and prove that birds evolved from dinosaurs.

Average: 7.2 (9 votes)
June 6th 2016

In this class, we will compare DNA from an individual against a reference human genome to find potentially disease-causing mutations. We will also learn how to identify the function of a protein even if it has been bombarded by so many mutations compared to similar proteins with known functions that it has become barely recognizable.

Average: 6.8 (5 votes)
May 30th 2016

Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part class is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to computational problems.

Average: 7.3 (8 votes)
May 30th 2016

Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part class is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to computational problems.

Average: 6 (6 votes)
May 30th 2016

This two-part course introduces the basic mathematical and programming principles that underlie much of Computer Science. Understanding these principles is crucial to the process of creating efficient and well-structured solutions for computational problems. To get hands-on experience working with these concepts, we will use the Python programming language.

Average: 5.6 (7 votes)
May 30th 2016

The course covers basic algorithmic techniques and ideas for computational problems arising frequently in practical applications: sorting and searching, divide and conquer, greedy algorithms, dynamic programming. We will learn a lot of theory: how to sort data and how it helps for searching; how to break a large problem into pieces and solve them recursively; when it makes sense to proceed greedily; how dynamic programming is used in genomic studies. You will practice solving computational problems, designing new algorithms, and implementing solutions efficiently (so that they run in less than a second).

Average: 10 (2 votes)
May 30th 2016

This two-part course builds upon the programming skills that you learned in our Introduction to Interactive Programming in Python course. We will augment those skills with both important programming practices and critical mathematical problem solving skills. These skills underlie larger scale computational problem solving and programming.

Average: 6.4 (12 votes)
May 24th 2016

In this interactive computer science course from MIT you’ll learn how to turn a processor into an entire computer system. Digital systems are at the heart of the information age in which we live, allowing us to store, communicate and manipulate information quickly and reliably. This computer science course is a bottom-up exploration of the abstractions, principles, and techniques used in the design of digital and computer systems. If you have a rudimentary knowledge of electricity and some exposure to programming, roll up your sleeves, join in and design a computer system!

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May 23rd 2016

After sequencing genomes, we would like to compare them. We will see that dynamic programming is a powerful algorithmic tool when we compare two genes (i.e., short sequences of DNA) or two proteins. When we "zoom out" to compare entire genomes, we will employ combinatorial algorithms.

Average: 5.3 (6 votes)
May 23rd 2016

Biologists still cannot read the nucleotides of an entire genome as you would read a book from beginning to end. However, they can read short pieces of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces. We will further learn about brute force algorithms and apply them to sequencing mini-proteins called antibiotics. Finally, you will learn how to apply popular bioinformatics software tools to sequence the genome of a deadly Staphylococcus bacterium.

Average: 10 (1 vote)
May 23rd 2016

Have you ever wished you knew how to program, but had no idea where to start from? This course will teach you how to program in Scratch, an easy to use visual programming language. More importantly, it will introduce you to the fundamental principles of computing and it will help you think like a software engineer.

Average: 9.5 (2 votes)
May 23rd 2016

In this course you will learn a whole lot of modern physics (classical and quantum) from basic computer programs that you will download, generalize, or write from scratch, discuss, and then hand in. Join in if you are curious (but not necessarily knowledgeable) about algorithms, and about the deep insights into science that you can obtain by the algorithmic approach.

Average: 9.3 (4 votes)
May 23rd 2016

This course begins a series of classes illustrating the power of computing in modern biology. Please join us on the frontier of bioinformatics to look for hidden messages in DNA without ever needing to put on a lab coat. After warming up our algorithmic muscles, we will learn how randomized algorithms can be used to solve problems in bioinformatics.

Average: 7.3 (6 votes)
May 23rd 2016

How do we infer which genes orchestrate various processes in the cell? How did humans migrate out of Africa and spread around the world? In this class, we will see that these two seemingly different questions can be addressed using similar algorithmic and machine learning techniques arising from the general problem of dividing data points into distinct clusters.

Average: 7.6 (5 votes)
May 23rd 2016

Transact-SQL is an essential skill for database professionals and developers working with SQL databases. This course takes you from your first SELECT statement to implementing transactional programmatic logic through a combination of expert instruction, demonstrations, and practical labs. The course consists of multiple modules, each exploring a key area of the Transact-SQL language, with a focus on querying and modifying data in Microsoft SQL Server or Azure SQL Database.

Average: 8.5 (2 votes)
May 23rd 2016

A good algorithm usually comes together with a good data structure that allows the algorithm to manipulate the data efficiently. In this course, we consider the common data structures that are used in various computational problems. We start from the most basic data structures such as arrays, queues, stacks, trees. We discuss typical situations where such data structures arise. We then consider two ways of implementing dictionaries — hash tables and binary search trees. These data structures are heavily used in programming languages and databases. In practice, any nontrivial program uses either a hash table or a binary search tree implicitly. Although those data structures are usually built-in or implemented in a library that you use, it is crucial to understand their advantages and shortcomings to efficiently apply one or another in your programs and sometimes even extend standard implementations. Finally, we discuss data structures that allow to perform queries like extracting the minimal value or checking whether two elements belong to the same set.

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