Algorithms

 

 


 

Customize your search:

E.g., 2017-09-20
E.g., 2017-09-20
E.g., 2017-09-20
Self Paced

Join us on the frontier of bioinformatics and learn how to look for hidden messages in DNA without ever needing to put on a lab coat. In the first half of this course, we'll investigate DNA replication, and ask the question, where in the genome does DNA replication begin? You will learn how to answer this question for many bacteria using straightforward algorithms to look for hidden messages in the genome.

No votes yet
Self Paced

Learn to program with Java in an easy and interactive way! In this introductory Java programming course, you will be introduced to powerful concepts such as functional abstraction, the object oriented programming (OOP) paradigm and Application Programming Interfaces (APIs). Examples and case studies will be provided so that you can implement simple programs on your own or collaborate with peers.

Average: 8 (9 votes)
Sep 25th 2017

Cloud computing systems today, whether open-source or used inside companies, are built using a common set of core techniques, algorithms, and design philosophies—all centered around distributed systems. Learn about such fundamental distributed computing "concepts" for cloud computing.

Average: 7.6 (5 votes)

Sep 25th 2017

How do Java programs deal with vast quantities of data? Many of the data structures and algorithms that work with introductory toy examples break when applications process real, large data sets. Efficiency is critical, but how do we achieve it, and how do we even measure it? In this course, you will use and analyze data structures that are used in industry-level applications, such as linked lists, trees, and hashtables.

Average: 6.3 (4 votes)
Sep 25th 2017

The primary topics in this part of the specialization are: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis).

Average: 6.8 (4 votes)
Sep 25th 2017

Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery.

Average: 7.8 (5 votes)

Sep 25th 2017

The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).

Average: 6.2 (6 votes)
Sep 25th 2017

With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm.

Average: 6 (7 votes)
Sep 25th 2017

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.

Average: 7.7 (9 votes)
Sep 25th 2017

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

Average: 5.9 (18 votes)
Sep 25th 2017

Case Study - Predicting Housing Prices
In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets.

Average: 7.4 (5 votes)

Sep 25th 2017

This course is for experienced C programmers who want to program in C++. The examples and exercises require a basic understanding of algorithms and object-oriented software.

Average: 4.8 (17 votes)