Online courses directory (726)
Learn how to program all the major systems of a robotic car from the leader of Google and Stanford's autonomous driving teams. This class will teach you basic methods in Artificial Intelligence, including: probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics. Extensive programming examples and assignments will apply these methods in the context of building self-driving cars. This course is offered as part of the Georgia Tech Masters in Computer Science. The updated course includes a final project, where you must chase a runaway robot that is trying to escape!
Try to picture yourself sitting down with your computer, ready to start developing a fully functional web application for the first time, available online for millions to use. “Where should I even begin? How long is this going to take me? Am I making any mistakes along the way?” The questions may leave you with an uneasy feeling that you will learn many lessons the hard way. In this intermediate course, Steve Huffman will teach you everything he wished he knew when he started building Reddit and, more recently, Hipmunk, as a lead engineer. Starting from the basics of how the web works, this course will walk you through core web development concepts such as how internet and browsers fit together, form validations, databases, APIs, integrating with other websites, scaling issues, and more; all of which form part of the knowledge it takes to build a web application of your own.
In this introduction to computer programming course, you’ll learn and practice key computer science concepts by building your own versions of popular web applications. You’ll learn Python, a powerful, easy-to-learn, and widely used programming language, and you’ll explore computer science basics, as you build your own search engine and social network.
In this introductory course, you'll learn and practice essential computer science concepts using the Java programming language. You'll learn about Object Oriented Programming, a technique that allows you to use code written by other programmers in your own programs. You'll put your new Java programming skills to the test by solving real-world problems faced by software engineers.
Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions. Machine learning brings together computer science and statistics to harness that predictive power. It’s a must-have skill for all aspiring data analysts and data scientists, or anyone else who wants to wrestle all that raw data into refined trends and predictions. This is a class that will teach you the end-to-end process of investigating data through a machine learning lens. It will teach you how to extract and identify useful features that best represent your data, a few of the most important machine learning algorithms, and how to evaluate the performance of your machine learning algorithms. This course is also a part of our Data Analyst Nanodegree.
This class is offered as CS6250 at Georgia Tech where it is a part of the [Online Masters Degree (OMS)](http://www.omscs.gatech.edu/). Taking this course here will not earn credit towards the OMS degree. This course covers advanced topics in Computer Networking such as Software-Defined Networking (SDN), Data Center Networking and Content Distribution. The course is divided into three parts: Part 1 is about the implementation, design principles and goals of a Computer Network and touches upon the various routing algorithms used in CN (such as link-state and distance vector). Part 2 talks about resource control and content distribution in Networking Applications. It covers Congestion Control and Traffic Shaping. Part 3 deals with the operations and management of computer networks encompassing SDN's (Software Defined Networks), Traffic Engineering and Network Security.
This class is offered as CS6290 at Georgia Tech where it is a part of the [Online Masters Degree (OMS)](http://www.omscs.gatech.edu/). Taking this course here will not earn credit towards the OMS degree. The course begins with a lesson on performance measurement, which leads to a discussion on the necessity of performance improvement. Pipelining, the first level of performance refinement, is reviewed. The weaknesses of pipelining will be exposed and explored, and various solutions to these issues will be studied. The student will learn hardware, software, and compiler based solutions to these issues.
This class is offered as CS6505 at Georgia Tech where it is a part of the [Online Masters Degree (OMS)](http://www.omscs.gatech.edu/). Taking this course here will not earn credit towards the OMS degree. In this course, we will ask the big questions, “What is a computer? What are the limits of computation? Are there problems that no computer will ever solve? Are there problems that can’t be solved quickly? What kinds of problems can we solve efficiently and how do we go about developing these algorithms?” Understanding the power and limitations of algorithms helps us develop the tools to make real-world computers smarter, faster and safer.
This mini-course is intended for students who would like a refresher on the basics of linear algebra. The course attempts to provide the motivation for "why" linear algebra is important in addition to "what" linear algebra is. Students will learn concepts in linear algebra by applying them in computer programs. At the end of the course, you will have coded your own personal library of linear algebra functions that you can use to solve real-world problems.
This course delivers a systematic overview of computer vision, emphasizing two key issues in modeling vision: space and meaning. We will study the fundamental theories and important algorithms of computer vision together, starting from the analysis of 2D images, and culminating in the holistic understanding of a 3D scene.
Programming-oriented course on effectively using modern computers to solve scientific computing problems arising in the physical/engineering sciences and other fields. Provides an introduction to efficient serial and parallel computing using Fortran 90, OpenMP, MPI, and Python, and software development tools such as version control, Makefiles, and debugging.
The goal of this course is to give you solid foundations for developing, analyzing, and implementing parallel and locality-efficient algorithms. This course focuses on theoretical underpinnings. To give a practical feeling for how algorithms map to and behave on real systems, we will supplement algorithmic theory with hands-on exercises on modern HPC systems, such as Cilk Plus or OpenMP on shared memory nodes, CUDA for graphics co-processors (GPUs), and MPI and PGAS models for distributed memory systems. This course is a graduate-level introduction to scalable parallel algorithms. “Scale” really refers to two things: efficient as the problem size grows, and efficient as the system size (measured in numbers of cores or compute nodes) grows. To really scale your algorithm in both of these senses, you need to be smart about reducing asymptotic complexity the way you’ve done for sequential algorithms since CS 101; but you also need to think about reducing communication and data movement. This course is about the basic algorithmic techniques you’ll need to do so. The techniques you’ll encounter covers the main algorithm design and analysis ideas for three major classes of machines: for multicore and many core shared memory machines, via the work-span model; for distributed memory machines like clusters and supercomputers, via network models; and for sequential or parallel machines with deep memory hierarchies (e.g., caches). You will see these techniques applied to fundamental problems, like sorting, search on trees and graphs, and linear algebra, among others. The practical aspect of this course is implementing the algorithms and techniques you’ll learn to run on real parallel and distributed systems, so you can check whether what appears to work well in theory also translates into practice. (Programming models you’ll use include Cilk Plus, OpenMP, and MPI, and possibly others.)