Top 22 Free Online Courses In Computer Science And Artificial Intelligence!

You must make yourself aware of the number of freely available resources on the Internet that can help you in developing your basic skills, courtesy computer science and artificial intelligence. You don't? Well, no problem! We are here to help!

1. Advanced Data Structures

-Data structures play a central role in modern computer science. You interact with data structures even more often than with algorithms. In addition, data structures are essential building blocks in obtaining efficient algorithms. This course covers major results and current directions of research in data structure.

2. Artificial Intelligence

-This course includes interactive demonstrations which are intended to stimulate interest and to help students gain intuition about how artificial intelligence methods work under a variety of circumstances.

3. Artificial Intelligence – Introduction to Robotics

-The purpose of this course is to introduce you to basics of modeling, design, planning, and control of robot systems. In essence, the material treated in this course is a brief survey of relevant results from geometry, kinematics, statics, dynamics, and control.

4. Artificial Intelligence – Natural Language Processing

-This course is designed to introduce students to the fundamental concepts and ideas in natural language processing (NLP), and to get them up to speed with current research in the area. It develops an in-depth understanding of both the algorithms available for the processing of linguistic information and the underlying computational properties of natural languages.

5. Artificial Intelligence – Machine Learning

-This course provides a broad introduction to machine learning and statistical pattern recognition.

6. Bits: The Computer Science of Digital Information

-This course focuses on information as quantity, resource, and property.

7. Building Mobile Applications

-This course teaches students how to build mobile apps for Android and iOS, two of today's most popular platforms, and how to deploy them in Android Market and the App Store. Students learn how to write native apps for Android using Eclipse and the Android SDK, how to write native apps for iPhones, iPod touches, and iPads using Xcode and the iOS SDK, and how to write web apps for both platforms.

8. Computational Camera and Photography

-A computational camera attempts to digitally capture the essence of visual information by exploiting the synergistic combination of task-specific optics, illumination, sensors and processing. In this course we will study this emerging multi-disciplinary field at the intersection of signal processing, applied optics, computer graphics and vision, electronics, art, and online sharing through social networks.

9. Computational Discrete Mathematics

-This course presents material in discrete mathematics and computation theory with a strong emphasis on practical algorithms and experiential learning.

10. Computer Language Engineering

-This course analyses issues associated with the implementation of high-level programming languages. Topics covered include: fundamental concepts, functions, and structures of compilers, basic program optimisation techniques, the interaction of theory and practice, and using tools in building software.

11. CS50, Harvard’s Introductory Computer Science Course

-This course teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, encapsulation, data structures, databases, memory management, security, software development, virtualisation, and websites.

12. Discrete Stochastic Processes

-Discrete stochastic processes are essentially probabilistic systems that evolve in time via random changes occurring at discrete fixed or random intervals. This course aims to help students acquire both the mathematical principles and the intuition necessary to create, analyse, and understand insightful models for a broad range of these processes.

13. Introduction to Algorithms

-This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasises the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems

14. Introduction to Computer Science and Programming

-It aims to provide students with an understanding of the role computation can play in solving problems. It also aims to help students, regardless of their major, to feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals.

15. Introduction to Electrical Engineering and Computer Science I

-This course provides an integrated introduction to electrical engineering and computer science, taught using substantial laboratory experiments with mobile robots.

16. Mathematics for Computer Science

-This course covers elementary discrete mathematics for computer science and engineering. It emphasises mathematical definitions and proofs as well as applicable methods. Topics include formal logic notation, proof methods; induction, well-ordering; sets, relations; elementary graph theory; integer congruences; asymptotic notation and growth of functions; permutations and combinations, counting principles; discrete probability.

17. Multicore Programming Primer

-It offers a series of lectures on parallel programming concepts as well as a group project providing hands-on experience with parallel programming. The students will have the unique opportunity to use the cutting-edge PLAYSTATION 3 development platform as they learn how to design and implement exciting applications for multicore architectures.

18. Performance Engineering of Software Systems

-This class is a hands-on, project-based introduction to building scalable and high-performance software systems. Topics include performance analysis, algorithmic techniques for high performance, instruction-level optimisations, cache and memory hierarchy optimisation, parallel programming, and building scalable distributed systems.

19. Principles of Digital Communications I

-The course serves as an introduction to the theory and practice behind many of today's communications systems. Topics covered include: digital communications at the block diagram level, data compression, Lempel-Ziv algorithm, scalar and vector quantisation, sampling and aliasing, the Nyquist criterion, PAM and QAM modulation, signal constellations, finite-energy waveform spaces, detection, and modeling and system design for wireless communication.

20. Responsible Computing

-This course is designed to help students (primarily incoming college freshmen) develop the foundational computing and information literacy skills that they will need to succeed in other courses. Topics Covered: Responsible Computing, Effective Computing, Safe Computing, and Information Literacy.

21. Understanding Computers and the Internet

-Designed for students who use computers and the Internet every day but don't fully understand how it all works, this course fills in the gaps. Through lectures on hardware, software, the Internet, multimedia, security, privacy, website development, programming, and more, this course "takes the hood off" of computers and the Internet so that students understand how it all works and why.

22. XML with Java

-This course introduces XML as a key enabling technology in Java-based applications. Students learn the fundamentals of XML and its derivatives, including DTD, SVG, XML Schema, XPath, XQuery, XSL-FO, and XSLT. Students also gain experience with programmatic interfaces to XML like SAX and DOM, standard APIs like JAXP and TrAX, and industry-standard software like Ant, Tomcat, Xerces, and Xalan.