Advanced Machine Learning and Signal Processing

Start Date: 02/07/2021

Course Type: Common Course

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About Course

>>> By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. Once enrolled you can access the license in the Resources area <<< This course, Advanced Machine Learning and Signal Processing, is part of the IBM Advanced Data Science Specialization which IBM is currently creating and gives you easy access to the invaluable insights into Supervised and Unsupervised Machine Learning Models used by experts in many field relevant disciplines. We’ll learn about the fundamentals of Linear Algebra to understand how machine learning modes work. Then we introduce the most popular Machine Learning Frameworks for python Scikit-Learn and SparkML. SparkML is making up the greatest portion of this course since scalability is key to address performance bottlenecks. We learn how to tune the models in parallel by evaluating hundreds of different parameter-combinations in parallel. We’ll continuously use a real-life example from IoT (Internet of Things), for exemplifying the different algorithms. For passing the course you are even required to create your own vibration sensor data using the accelerometer sensors in your smartphone. So you are actually working on a self-created, real dataset throughout the course. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link

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Course Introduction

Advanced Machine Learning and Signal Processing This course introduces advanced signal processing techniques for use in data science and machine learning applications. It covers topics such as fractional point-enhancement, point-source phase inverting, noise-cancellation, and band-pass filters. The course is suitable for undergraduate and graduate students. The course requires basic knowledge of python programming (both the basic and advanced features) and a recent linux system (should work with most recent laptops). The course requires you to have a recent linux system with a recent stable kernel and a recent notebook (or desktop). Learning Goals: After this course, you will be able to: - Implement simple algorithms for point-source and band-pass filters - Understand how to use and extend these algorithms - Manipulate a signal using a custom detector - Perform point-source and band-pass filters in a simple and robust fashion - Leverage sophisticated machine learning algorithms Suggested Reading: To get the most out of this course, you should have a basic understanding of python programming and linux system internals. You should have experience in programming in python (including python setuptools), building machine learning based applications, and have some knowledge of basic statistics. You should have experience in image analysis, signal processing, and numerical analysis (NAMD). You should have experience in machine learning and/or statistics in python (including numerical algorithms), and have some knowledge of things like convolutional neural networks, L2 and

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Financial signal processing For a long time, financial signal processing technologies have been used by different hedge funds, such as Jim Simon's Renaissance Technologies. However, hedge funds usually do not reveal their trade secrets. Some early research results in this area are summarized by R.H. Tütüncü and M. Koenig and by T.M. Cover, J.A. Thomas. A.N. Akansu and M.U. Torun published the book in financial signal processing entitled "A Primer for Financial Engineering: Financial Signal Processing and Electronic Trading." An edited volume on the subject with the title "Financial Signal Processing and Machine Learning" was also published. There were two special issues of IEEE Journal of Selected Topics in Signal Processing published on Signal Processing Methods in Finance and Electronic Trading in 2012, and on Financial Signal Processing and Machine Learning for Electronic Trading in 2016 in addition to the special section on Signal Processing for Financial Applications in IEEE Signal Processing Magazine appeared in 2011.
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