Keep learning II : Robot stories

After long time without a proper learning course it is time to prepare the tools to learn robotic stuff as easy as possible. In this post I will show my next steps and I would like to organize all the things I would like to have time to learn.

Coursera courses

Control of Mobile Robots

• Workload: 5-7 hours/week
• Topic: Learn about how to make mobile robots move in effective, safe, predictable, and collaborative ways using modern control theory.

Machine Learning

• Workload: 5-7 hours/week
• Topic: Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.

Machine learning

• Workload: 5-7 hours/week
• Topic: Why write programs when the computer can instead learn them from data? In this class you will learn how to make this happen, from the simplest machine learning algorithms to quite sophisticated ones. Enjoy!

Artificial Intelligence Planning

• Workload: 3-8 hours/week
• Topic:The course aims to provide a basic grounding in artificial intelligence techniques for planning, with an overview of the wide spectrum of different problems and approaches, including their underlying theory and their applications.

Natural Language Processing

• Workload: 8-10 hours/week
• Topic: Have you ever wondered how to build a system that automatically translates between languages? Or a system that can understand natural language instructions from a human? This class will cover the fundamentals of mathematical and computational models of language, and the application of these models to key problems in natural language processing.

Algorithms: Design and Analysis, Part 1

• Workload: 5-7 hours/week
• Topic: In this course you will learn several fundamental principles of algorithm design: divide-and-conquer methods, graph algorithms, practical data structures (heaps, hash tables, search trees), randomized algorithms, and more.

Creative, Serious and Playful Science of Android Apps

• Workload: 5-10 hours/week
• Topic: This course introduces the fundamental computer science principles that power todayÔÇÖs apps. You will also learn to create your own Android app using Java and standard software development tools.

Programming Languages

• Workload: Workload: 8-16 hours/week
• Topic: Investigate the basic concepts behind programming languages, with a strong emphasis on the techniques and benefits of functional programming. Use the programming languages ML, Racket, and Ruby in ways that will teach you how the pieces of a language fit together to create more than the sum of the parts. Gain new software skills and the concepts needed to learn new languages on your own.

Human-Computer Interaction

• Workload: Workload: 10-12 hours/week
• Topic: Helping you build human-centered design skills, so that you have the principles and methods to create excellent interfaces with any technology.

Writing in the Sciences

• Workload: 4-8 hours/week
• Topic: This course teaches scientists to become more effective writers, using practical examples and exercises. Topics include: principles of good writing, tricks for writing faster and with less anxiety, the format of a scientific manuscript, and issues in publication and peer review.

English Composition I: Achieving Expertise

• Workload: 6-8 hours/week
• Topic:You will gain a foundation for college-level writing valuable for nearly any field. Students will learn how to read carefully, write effective arguments, understand the writing process, engage with others' ideas, cite accurately, and craft powerful prose. We will create a workshop environment.

Udacity courses

Software Debugging (cs259)

• Topic: At the end of this course you will have a solid understanding about systematic debugging, will know how to automate debugging and will have built several functional debugging tools in Python.

Introduction to Artificial Intelligence

• Topic: The objective of this class is to teach you modern AI. You will learn about the basic techniques and tricks of the trade. We also aspire to excite you about the field of AI.

Artificial Intelligence for Robotics

• Topic: 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.

Standford University courses

Artificial Intelligence for Robotics

• Topic: 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.

Introduction to robotics

• Topic: 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.The course is presented in a standard format of lectures, readings and problem sets. There will be an in-class midterm and final examination. These examinations will be open book. Lectures will be based mainly, but not exclusively, on material in the Lecture Notes book. Lectures will follow roughly the same sequence as the material presented in the book, so it can be read in anticipation of the lecturesÔÇ¿ÔÇ¿.

Advanced Robotics

• Topic: This course focuses on advanced control methodologies and novel design techniques for complex human like robotic systems. It provides an extensive coverage of the task oriented operational space formulation, and discusses its application to the challenges of interactive whole body control of humanoid robots

Algorithms: Design and Analysis, Part 2

• Topic: 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. YouÔÇÖll learn what NP-completeness and the famous ÔÇ£P vs. NPÔÇØ problem means for the algorithm designer. Finally, we will 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!

Other Universities courses

Bio-Inspired Robotics

• Topic: Biologically-inspired problem solving has become a hot topic in the last ten years. The approach tries to bring inspirations from biology and animal society to make useful contributions to design of algorithm for solving problems in regular life. This course tries to summarize the related researches to artificial intelligence and robotics field. The students that take this course will learn to look at the engineering problems in a different way than the classical methods. This course will also address to some extent how computer science and robotics can contribute to a better understanding of biological systems.

Conclusion

As you can see, there are plenty of courses you can join. There are courses made by university teachers and the only problem, for my, is that there are too much interesting courses and they are time-consuming. Please, let me know if you find other interesting courses.