AI for Signal Processing
Last day of registration: 3rd July, 2024
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Profile of the Instructor(s)
Ms Shanthi is an experienced educator and online content developer at MathWorks, specializing in MATLAB for signal processing. She holds a master’s degree in Signal Processing. Shanthi's professional interests span across subjects including Digital Signal Processing, Image Processing, Neural Networks, and Wireless Communication. Her scholarly contributions include publications in international journals on topics such as Video Compression Standards and their implementation in MATLAB and Background Subtraction Techniques in Image Processing. She started her career by training engineering graduates and has developed learning content for specialization courses using instructional design principles. At MathWorks, she creates online learning content related to MATLAB for signal processing and AI.
Ms Priyanka is an online content developer at MathWorks focused on learning content related to physical modeling. She holds a master's degree in Power Systems and is pursuing her part-time Ph.D in Electrical Engineering. Her research areas are implementation of Machine Learning and Deep Learning techniques in Wind Power Systems for Power System Stability, Fault Detection, and Fault Classification. She has published papers in international journals and conferences on Deep Learning techniques for Wind Power Prediction and Fault Classification. Module Details
Module name |
Concepts covered |
Learning outcomes |
| AI for Signal Processing | 1. AI Workflow 2. Application areas | Familiarize with the different stages in AI workflow. |
| Signal Generation and Labeling | 1. Signal Generation 2. Signal Management 3. Signal Labeling |
1. Organize datasets into structured formats, access them using appropriate functions, and manage data effectively for AI applications. 2. Label time domain and frequency-domain features in complex signals, using standard signal processing terminology. |
| Signal Preprocessing | 1. Signal Analysis 2. Resampling 3. Outlier Detection and Handling 4. Denoising 5. Filtering | Preprocess signals by identifying and handling outliers and filling missing data points, ensuring clean and uniform for analysis. |
| Feature Engineering | 1. Feature Extraction 2. Feature Transformation 3. Feature Selection | Apply and evaluate various feature engineering techniques to improve the performance and accuracy of machine learning models. |


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