Continual Learning Course
ContinualAIWikiAvalanche Mailing-list
  • Continual Learning: On Machines that can Learn Continually
  • Background
    • 🔡Prerequisites
    • 🛠️Tools & Setup
    • 📑Course Details
  • Lectures
    • 📍Introduction & Motivation
    • 📍Understanding Catastrophic Forgetting
    • 📍Scenarios & Benchmarks
    • 📍Evaluation & Metrics
    • 📍Methodologies [Part 1]
    • 📍Methodologies [Part 2]
    • 📍Methodologies [Part 3], Applications & Tools
    • 📍Frontiers in Continual Learning
  • Invited & Extra Lectures
    • 💻Avalanche Dev Day
    • 🔮Invited Talks
  • Resources
    • 📚Course Materials
    • 🔀Additional Material
  • About Us
    • 👨‍🏫Your Instructor
    • 🆘Teaching Assistants
  • Useful Links
    • Avalanche
    • Forum
    • Colab
    • Wiki
    • Open World Lifelong Learning Course
    • ContinualAI
    • Join us on Slack!
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  1. Background

Prerequisites

Things you Should Know Before Enrolling

PreviousContinual Learning: On Machines that can Learn ContinuallyNextTools & Setup

Last updated 3 years ago

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This course has been designed for Graduate and PhD Students that have never been exposed to Continual Learning. However, it assumes basic knowledge in Computer Science (Bachelor level) and Machine Learning. In particular we assume basic knowledge in Deep Learning.

For students do not have this background we suggest to follow at least an introductory Machine Learning course such the one offered by .

We also assume basic hands-on knowledge about:

  • Anaconda, Python and PyCharm

  • Python Notebooks

  • Google Colaboratory

  • Git and GitHub

  • PyTorch

Make sure you learn the basics of these tools and languages as they will be used extensively across the course.

🔡
Andrew Ng at Cursera