kr2,000 – kr8,000

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Location

OsloMet campus

Pilestredet 52

Room E513

0167

Norway

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Refund Policy

Refund Policy

Refunds up to 7 days before event

Event description

Description

Modern machine learning (ML) is a key to develop intelligent systems and analyze data in science and engineering. Today it provides impressive results in many fields, enabling intelligent technologies such as artificial voice assistants, and smart services such as optimized energy consumption. ML systems are considered to be one of the largest growth markets with a vast collection of tools and approaches.

Successful application of ML methods to real-life challenges require expertise that many do not possess. However, many may be unaware of that most ML methods leverage the same building blocks and share the same basic concepts. Therefore, key to applying machine learning lays in understanding the basic formulations, relating them with prototypical study cases, reasoning on the situations when they are most appropriate.

We here provide a modified version of the long-standing successful courses taught at the University of Genova and MIT, and focus on the fundamental methods of modern ML, providing participants with the knowledge needed to get started with ML. The sessions on theoretical and algorithmic aspects will be complemented by hands-on training using Jupyter notebooks. The participants will also have an opportunity for a face-to-face session with the instructors to discuss individually relevant ML challenges.


PARTICIPANT'S TAKEAWAYS:

  • Learn how to formulate problems as machine learning tasks and to design effective solution strategies

  • Understand the fundamental machine learning concepts and methods

  • Obtain an overview of the main supervised and unsupervised learning algorithms, including neural networks, with analysis of strengths and weaknesses

  • Explore Python/Jupyter notebooks and relevant libraries for machine learning

  • Obtain hands-on experience using real-life data and specific use cases, both with stationary and temporal extent


TARGET AUDIENCE:

This course provides a better understanding of machine learning fundamental and is tailored towards professionals with a technical competence and basic programming skills. This course describes key concepts, algorithms, and practical knowledge to professionals who are starting, or need to brush up machine learning skills, and provides participants with core knowledge to succeed to the advanced level.


SCHEDULE:

Day 1


09:00-14:00 (4 hours): Theoretical classes with Jupyter notebooks

  • Introduction to ML
  • Supervised learning: classification and regression; linear/non linear models: features maps/kernels and neural nets; model and feature selection
  • Unsupervised learning: clustering; dimensionality reduction

​14:00-17:00 (2.5 hours): Hands-on activity with Jupyter notebook: ML concepts


Day 2


09:00-14:00 (4 hours): A case-study: time series

  • Introduction to the problem
  • Hands-on activity with Jupyter notebook

14:00-17:00 (3 hours): Face2face sessions with the instructors (up to 30 min per participant)


INSTRUCTORS:

Nicoletta Noceti​ is an Assistant Professor in Computer Science at the University of Genova. Her research activity is mainly focused on the design and development of visual computing methods combining computer vision and machine learning for images and videos understanding, with applications to human-machine interaction, robotics, and Ambient Assisted Living. Recently, she has also been working on the analysis of time data, in the context of predictive maintenance for industrial applications. She is currently co-instructor for machine and deep learning courses both at master level and at PhD level.


Francesca Odone is an Associate Professor in Computer Science at the University of Genova. Her research interests are in the fields of computer vision and machine learning, including multi-resolution signal processing, feature extraction, feature selection and data-driven representations for visual data. She has been involved in various research projects and acted as a scientific coordinator of technology transfer contracts with SMEs, large companies and hospitals. For over ten years she has taught courses on data science topics (mainly machine learning, signal and image processing, computer vision) both at master level and at PhD level.


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Date and Time

Location

OsloMet campus

Pilestredet 52

Room E513

0167

Norway

View Map

Refund Policy

Refunds up to 7 days before event

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