Start Date: 02/07/2021
Course Type: Common Course |
Course Link: https://www.coursera.org/learn/advanced-machine-learning-signal-processing
>>> 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 ibm.biz/badging.
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. |
Signal processing | "Analog discrete-time signal processing" is a technology based on electronic devices such as sample and hold circuits, analog time-division multiplexers, analog delay lines and analog feedback shift registers. This technology was a predecessor of digital signal processing (see below), and is still used in advanced processing of gigahertz signals. |
Machine learning | Machine learning tasks are typically classified into three broad categories, depending on the nature of the learning "signal" or "feedback" available to a learning system. These are |
Digital signal processing | Digital signal processing and analog signal processing are subfields of signal processing. DSP applications include audio and speech signal processing, sonar, radar and other sensor array processing, spectral estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control of systems, biomedical engineering, seismic data processing, among others. |
Signal processing | Signal processing is an enabling technology that encompasses the fundamental theory, applications, algorithms, and implementations of processing or transferring information contained in many different physical, symbolic, or abstract formats broadly designated as "signals". It uses mathematical, statistical, computational, heuristic, and linguistic representations, formalisms, and techniques for representation, modelling, analysis, synthesis, discovery, recovery, sensing, acquisition, extraction, learning, security, or forensics. |
Institute for Language and Speech Processing | ILSP carries out applied research in Man-Machine Interfaces, Machine Learning, Speech Processing, Text Processing, Theoretical and Computational Linguistics and Language Learning Technologies. Expertise used by the Institute includes basic fields as Natural Language Processing, Digital Signal Processing and Pattern Recognition. Its mission is mainly to support basic research, promoting on the other hand the development of new products in the form of laboratory prototypes. |
Audio signal processing | Historically, before the advent of widespread digital technology, ASP was the only method by which to manipulate a signal. Since that time, as computers and software became more advanced, digital signal processing has become the method of choice. |
Signal processing | The concept of discrete-time signal processing also refers to a theoretical discipline that establishes a mathematical basis for digital signal processing, without taking quantization error into consideration. |
International Conference on Machine Learning | The conference attracts leading innovations in the field of machine learning. ICML is a top tier conference, and is one of the two most influential conferences in Machine Learning (along with Conference on Neural Information Processing Systems). |
Multidimensional signal processing | In signal processing, multidimensional signal processing covers all signal processing done using multidimensional signals and systems. While multidimensional signal processing is a subset of signal processing, it is unique in the sense that it deals specifically with data that can only be adequately detailed using more than one dimension. In m-D digital signal processing, useful data is sampled in more than one dimension. Examples of this are image processing and multi-sensor radar detection. Both of these examples use multiple sensors to sample signals and form images based on the manipulation of these multiple signals. |
Signal processing | In communication systems, signal processing may occur at: |
Noise (signal processing) | In signal processing, noise is a general term for unwanted (and, in general, unknown) modifications that a signal may suffer during capture, storage, transmission, processing, or conversion. |
Active learning (machine learning) | Recent developments are dedicated to hybrid active learning and active learning in a single-pass (on-line) context, combining concepts from the field of Machine Learning (e.g., conflict and ignorance) with adaptive, incremental learning policies in the field of Online machine learning. |
Audio signal processing | A digital representation expresses the pressure wave-form as a sequence of symbols, usually binary numbers. This permits signal processing using digital circuits such as microprocessors and computers. Although such a conversion can be prone to loss, most modern audio systems use this approach as the techniques of digital signal processing are much more powerful and efficient than analog domain signal processing. |
Digital signal processing | Digital signal processing can involve linear or nonlinear operations. Nonlinear signal processing is closely related to nonlinear system identification and can be implemented in the time, frequency, and spatio-temporal domains. |
Machine learning | Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves `rules’ to store, manipulate or apply, knowledge. The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learners that commonly identify a singular model that can be universally applied to any instance in order to make a prediction. Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems. |
Signal processing | Discrete-time signal processing is for sampled signals, defined only at discrete points in time, and as such are quantized in time, but not in magnitude. |
Multidimensional signal processing | Typically, multidimensional signal processing is directly associated with digital signal processing because its complexity warrants the use of computer modelling and computation. A multidimensional signal is similar to a single dimensional signal as far as manipulations that can be performed, such as sampling, Fourier analysis, and filtering. The actual computations of these manipulations grow with the number of dimensions. |
Adversarial machine learning | Adversarial machine learning is a research field that lies at the intersection of machine learning and computer security. It aims to enable the safe adoption of machine learning techniques in adversarial settings like spam filtering, malware detection and biometric recognition. |
Journal of Machine Learning Research | In response to the prohibitive costs of arranging workshop and conference proceedings publication with traditional academic publishing companies, the journal launched a proceedings publication arm in 2007 and now publishes proceedings for several leading machine learning conferences including the International Conference on Machine Learning, COLT, AISTATS, and workshops held at the Conference on Neural Information Processing Systems. |