MathWorks at University of Cambridge: Machine & Deep Learning Workshop Series

This workshop series on Machine and Deep Learning in MATLAB is being presented by MathWorks at the Department of Engineering and the Centre for Mathematical Sciences, Cambridge on the 19th & 26th February from 4-6pm.

Workshop #1: Practical applications of Machine Learning
Machine learning is a data analytics technique that teaches computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model.

Using MATLAB, engineers and other domain experts have deployed thousands of applications for predictive maintenance, sensor analytics, finance, and communication electronics.

In this hands-on workshop, you will:

  • Learn the fundamentals of machine learning and understand terms like “supervised learning”, “feature extraction”, and “hyperparameter tuning”
  • Build and evaluate machine learning models for classification and regression
  • Perform automatic hyperparameter tuning and feature selection to optimize model performance
  • Apply signal processing and feature extraction techniques
  • Generate automated C/C++ code for embedded and high-performance applications (demonstration only)

Workshop #2: Practical applications of Deep Learning
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:

  • Learn the fundamentals of Deep Learning and understand terms like “layers”, “networks”, and “loss”
  • Access and explore various pretrained models
  • Use transfer learning to build a network for image classification
  • Build a deep network from scratch for image classification
  • Learn how to evaluate the network and improve its accuracy
  • Use LSTM Networks for time series forecasting

Who Should Attend
The session is tailored for students and academics that are interested in practical applications of AI techniques.

About the Presenters
Dr Francesco Ciriello is an Education Customer Success Engineer at MathWorks specialized in machine learning and image processing applications for embedded systems.

Dr Julia Hoerner is the EMEA Deep Learning Academic Liason Manager at MathWorks.

To register for the workshop please click here.