MATLAB and Simulink Seminars

2019 MIT IAP

W31-301, MIT


Please join us for a complimentary MATLAB seminar. Faculty, staff, researchers and students are all welcome to attend

Session 1: Introduction to MATLAB: Programming and Problem Solving (2 hours)

Presenter: Debanjana Mukherjee, MW Training

MATLAB is a high-level language that allows you to quickly perform computation and visualization through easy-to-use programming constructs. This hands-on lab presents the essentials you need to use MATLAB for your classes or research.

In this hands-on workshop, attendees will learn how to import data from an external file, plot the data over time, then perform some analysis to view the data trends. You’ll learn how to write a MATLAB script and publish it to a format for sharing, such as HTML. You’ll also learn how to write your own MATLAB functions, use flow control, and create loops.

By the end of the session, you’ll have learned to create an application in MATLAB.

Key topics include:

  • Navigating the MATLAB desktop
  • Working with variables in MATLAB
  • Calling MATLAB functions
  • Importing and extracting data
  • Visualizing data
  • Conducting computational analysis
  • Fitting data to a curve
  • Automating analysis with scripts
  • Publishing MATLAB programs
  • Programming in MATLAB

Note: Attendees should bring a laptop to this hands-on lab.

Session 2: Practical Applications of Deep Learning – a Hands-on MATLAB Workshop (3 ½ hours)

Presenter: Pitambar Dayal, Technical Marketing

Deep learning achieves human-like accuracy for many tasks considered algorithmically unsolvable with traditional machine learning. It is frequently used to develop applications such as face recognition, automated driving, and image classification.

In this hands-on workshop, you will write code and use MATLAB to:

  • Learn the fundamentals of deep learning and understand terms like “layers”, “networks”, and “loss”
  • Build a deep network that can classify your own handwritten digits
  • Access and explore various pretrained models
  • Use transfer learning to build a network that classifies different types of food
  • Use LSTM Networks for time series forecasting.
  • Train deep learning networks on GPUs in the cloud
  • Learn how to use code generation technology to accelerate inference performance
  • Learn how to improve the accuracy of deep networks

Registration closed