To accommodate heterogeneous tasks in Internet of Things (IoT), a new communication and computing paradigm termed mobile edge computing emerges that extends computing services from the cloud to edge, but at the same time exposes new challenges on security. The present paper studies online security-aware edge computing under jamming attacks. Leveraging online learning tools, novel algorithms abbreviated as SAVE-S and SAVE-A are developed to cope with the stochastic and adversarial forms of jamming, respectively. Without utilizing extra resources such as spectrum and transmission power to evade jamming attacks, SAVE-S and SAVE-A can select the most reliable server to offload computing tasks with minimal privacy and security concerns. It is analytically established that without any prior information on future jamming and server security risks, the proposed schemes can achieve ${\cal O}\big(\sqrt{T}\big)$ regret. Information sharing among devices can accelerate the security-aware computing tasks. Incorporating the information shared by other devices, SAVE-S and SAVE-A offer impressive improvements on the sublinear regret, which is guaranteed by what is termed "value of cooperation." Effectiveness of the proposed schemes is tested on both synthetic and real datasets.

OpenCV is a native cross-platform C library for Computer Vision, Machine Learning, and image processing. It is increasingly being adopted for development in Python. This course features some trending applications of vision and deep learning and will help you master these techniques. You will learn how to retrieve structure from motion (sfm) and you will also see how we can build an application to capture 2D images and join them dynamically to achieve street views by capturing camera projection angles and relative image positions. You will also learn how to track your head in 3D in real-time, and perform facial recognition against a goldenset.

If you're an office worker, student, administrator, or just want to become more productive with your computer, programming will allow you write code that can automate tedious tasks. This course follows the popular (and free!) book, Automate the Boring Stuff with Python. Automate the Boring Stuff with Python was written for people who want to get up to speed writing small programs that do practical tasks as soon as possible. You don't need to know sorting algorithms or object-oriented programming, so this course skips all the computer science and concentrates on writing code that gets stuff done. This course is for complete beginners and covers the popular Python programming language.

About this course: Do you want to know how robots work? Are you interested in robotics as a career? Are you willing to invest the effort to learn fundamental mathematical modeling techniques that are used in all subfields of robotics? If so, then the "Modern Robotics: Mechanics, Planning, and Control" specialization may be for you. This specialization, consisting of six short courses, is serious preparation for serious students who hope to work in the field of robotics or to undertake advanced study.

Core ML is an exciting new framework that makes running various machine learning and statistical models on macOS and iOS feel natively supported. The framework helps developers integrate already prepared statistical and machine learning models into their apps. You will now be able to create applications that have machine learning functionality built in. Developers want to learn how to use the features inside Core ML to make their applications smarter when explored by users. These videos will show you just how to integrate machine learning into real-world applications.

In this online Python course from O'Reilly Media, you will learn how to program with the popular development language. This tutorial is designed for the beginner, and you do not need to have any experience at all with programming or development in order to learn how to program with Python using this video tutorial. Some of the topics that this course covers throughout the ultimate Python for beginners training include installing Python, data types and creating variables, input and output, decision making and repetition, iterators, list comprehension and functions. He also covers variable scope, modules - creating and using pre-built ones, object oriented programming, inheritance, exception handling and using data structures. By the completion of this python for beginners video based training course on Python programming, you will be comfortable with Python and how to apply it to developing applications.

About this course: Mathematical thinking is crucial in all areas of computer science: algorithms, bioinformatics, computer graphics, data science, machine learning, etc. In this course, we will learn the most important tools used in discrete mathematics: induction, recursion, logic, invariants, examples, optimality. We will use these tools to answer typical programming questions like: How can we be certain a solution exists? Am I sure my program computes the optimal answer? Do each of these objects meet the given requirements?

Welcome! "Machine Learning Prerequisites: Python Pandas & Matplotlib" is an excellent choice for both beginners and experts looking to expand their knowledge in Machine Learning field. Data Analysis is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses. Machine Learning Prerequisites: Python Pandas & Matplotlib offers in-depth video tutorials in which we'll dive into tons of different datasets, short and long, broken and pristine. I'll take you step-by-step through Data Analysis process using the most powerful python libraries (Numpy, Pandas and Matplotlib), from installation to visualization! .

Motivation: Post-database searching is a key procedure in peptide dentification with tandem mass spectrometry (MS/MS) strategies for refining peptide-spectrum matches (PSMs) generated by database search engines. Although many statistical and machine learning-based methods have been developed to improve the accuracy of peptide identification, the challenge remains on large-scale datasets and datasets with an extremely large proportion of false positives (hard datasets). A more efficient learning strategy is required for improving the performance of peptide identification on challenging datasets. Results: In this work, we present an online learning method to conquer the challenges remained for exiting peptide identification algorithms. We propose a cost-sensitive learning model by using different loss functions for decoy and target PSMs respectively. A larger penalty for wrongly selecting decoy PSMs than that for target PSMs, and thus the new model can reduce its false discovery rate on hard datasets. Also, we design an online learning algorithm, OLCS-Ranker, to solve the proposed learning model. Rather than taking all training data samples all at once, OLCS-Ranker iteratively feeds in only one training sample into the learning model at each round. As a result, the memory requirement is significantly reduced for large-scale problems. Experimental studies show that OLCS-Ranker outperforms benchmark methods, such as CRanker and Batch-CS-Ranker, in terms of accuracy and stability. Furthermore, OLCS-Ranker is 15--85 times faster than CRanker method on large datasets. Availability and implementation: OLCS-Ranker software is available at no charge for non-commercial use at https://github.com/Isaac-QiXing/CRanker.

This course provides both theoretical knowledge and practical skills in Big Geospatial Data Analysis with Google Earth Engine. In this course, you will be given hands on practical exercises to master analyzing big geospatial data on the cloud. You will learn to access, process and analyze satellite data including Landsat, MODIS, and Sentinel and others using an open source platform. You will also learn to classify satellite images using machine learning algorithms. You will also have access to the lab exercise scripts as part of this course.