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AI is everywhere. It's not just powering applications like smart assistants, machine translation, and automated driving, it's also giving engineers and scientists a set of techniques for tackling common tasks in new ways.

That’s because AI is transforming engineering in nearly every industry and application area. Beyond automated driving, AI is also used in models that predict machine failure, indicating when they will require maintenance; health and sensor analytics such as patient monitoring systems; and robotic systems that learn and improve directly from experience.

In this webinar series, we will learn and discuss on how to use MATLAB and Simulink explore the AI workflow involves preparing the data, creating a model, designing the system on which the model will run, and deploying to hardware or enterprise systems.

Episode 1: Cleaning and Analyzing Real World Sensor Data
Episode 2: Training and Validating for your In-House AI Applications
Episode 3: Making your AI Applications accessible to your business (Deployment and Enterprise Integration)
 

Episode 1: 

Cleaning and Analyzing Real World Sensor Data
27 October 20 | 15:00 - 16:00 | Online via WebEx
 
Sensor data provides valuable insight into how products and systems perform in the real-world. However, this data often contains artefacts that make it challenging to analyze, such as missing data, outliers, noisy data, and non-uniform sampling rates. These challenges can be compounded by a large amount of data from multiple data loggers, tests, systems, etc.
 
Watch this webinar to learn about new MATLAB features for working with sensor data, including:
 
  • MATLAB data types for working with time series sensor data
  • Working with large collections of telemetry data (big data)
  • Detecting and handling outliers, using preprocessing functions and Live Tasks
  • Smoothing and filtering noisy data, including spectral analysis with the Signal Analyzer app
  • Documenting analyses with the MATLAB Live Editor

Episode 2: 

Training and Validating for your In-House AI Applications
03 November 20 | 15:00 - 16:00 | Online via WebEx
 
  1. Why is my model’s accuracy getting worse?
  2. What is the difference between a training dataset and testing dataset?
  3. What are validation datasets used for?
  4. How can I improve my model without overfitting it?
Machine learning is all about fitting models to data. This process typically involves using an iterative algorithm that minimizes the model error. The parameters that control a machine learning algorithm’s behavior are called hyperparameters. Depending on the values you select for your hyperparameters, you might get a completely different model. So, by changing the values of the hyperparameters, you can find different, and hopefully better, models.
 

Episode 3: 

Making your AI Applications accessible to your business
(Deployment and Enterprise Integration)
10 November 20 | 15:00 - 16:00 | Online via WebEx
 
One of the last steps in building your own AI Application is to share and deploy it for your colleagues to use. MATLAB and Simulink have a feature to deploy your AI Models to Production Setting.
 
Highlights:
  • A detailed example of a deployment workflow
  • Demonstration of the RESTful/JSON interface
  • Demonstration of the web management dashboard
  •  Demonstration of MATLAB Production Server integration with third-party visualization too

Save your spot!

 

 

Speakers:

Sruthi Geetha

Sruthi Geetha is a Customer Success Engineer at TechSource Systems, Singapore. She works to improve the adoption of MATLAB and toolboxes in universities around South East Asia.

Prior to joining TechSource Systems, Sruthi was with MathWorks, the developer of MATLAB and Simulink software. She had spent 3 years at MathWorks, India, with experience in Customer Training, Customer Success Engineering and Technical Support. Her expertise in MathWorks tools, and background in Controls and Instrumentation, has helped multiple customers build their technical applications. Sruthi received her Master of Technology in Instrumentation and Control Engineering from National Institute of Technology, Tiruchirappalli, India and Bachelor of Technology in Chemical Engineering.

She has built her forte in Data Analysis with MATLAB, Control Systems Design, Robotics, Model Based Design using Simulink and Simscape, MATLAB/Simulink Algorithm for Automatic Code Generation and Optimization with MATLAB.

 

Siti Safwana

Siti Safwana is an Application Engineer at TechSource Systems. She is specialized in the field of image processing, computer vision, machine learning and deep learning with MATLAB. Before joining TechSource Systems in 2018, she worked at Techloyce in Selangor as an CRM and ERP Consultant in IT field.
 
Siti Safwana holds a Meng in Science in Electronic from Universiti Teknikal Malaysia Melaka and BEng in Computer Communication System Engineering from Universiti Putra Malaysia. She doing research in computer vision field in domain of depth/disparity map and 3D reconstruction for her MEng project at UTeM under department of Faculty of Electronic and Computer Engineering.

 

Kevin Chng

Kevin Chng is the LTC Application Engineer at Techsource Systems where he successfully assists customers  from financial sector to adopt finance and enterprise solutions. He specializes in finance modelling, application deployment and data analytics.  

Kevin has conducted seminars and workshops at various banks/hedge funds, government research agency and universities. He is one of the active contributors to MATLAB community.  

He holds a Bachelor of Chemical Engineering with Honors (1st Class) Degree from Universiti Malaysia Sabah, Malaysia. 

 

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