AI for Signal Processing

Last day of registration: 3rd July, 2024


This product is currently out of stock and unavailable.

SKU: 6th July, 2024 Categories: ,

Description of the Course

This workshop offers a comprehensive, hands-on approach to signal processing techniques designed specifically for AI applications. It emphasizes practical skills in organizing, managing, and analyzing signals, ensuring that participants are actively involved with the content from the start. The workshop further covers techniques of preprocessing signal data, including outlier handling and missing data imputation, to enhance data quality. Additionally, the workshop includes practical sessions on feature extraction, where participants will apply statistical, spectral, and wavelet analysis methods specifically designed for different types of signals. They will also engage directly in classifying signal data using a variety of classifiers and conducting performance evaluations to understand the outcomes.


Mode of Workshop : Online

Date of Workshop : 6th July, 2024

Timings: 10:00 a.m. to 01:00 p.m. (IST)

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.

Fees for the Workshop

Fees :

Students : Rs 236 (Rs 200 + 18% GST)

Faculty : Rs 590 (Rs 500 + 18% GST)

Industry : Rs 1180 (Rs 1000 + 18% GST)

Intended Audience

Engineering Students, Researchers and Industry professionals working on Signal Processing and AI Applications.


Any Undergraduate or Masters Engineering Students, Researchers and Industry professionals working on Signal Processing and AI Applications can attend this workshop.


Basic MATLAB knowledge, Basics of Signal processing.


Certificates will be provided to all the participants who attends the workshop


There are no reviews yet.

Be the first to review “AI for Signal Processing”