Advancing Continual Learning For Robotics Applications

Continual Learning, Incremental Learning, Brain-Inspired lifelong learning Algorithms, Robotics Applications

What you'll learn

Learn deep learning algorithms through simple, math-focused explanations.

Explore continual learning with in-depth analysis of algorithms, scenarios, and strategies.

Focus on real-world applications, especially in robotics.

Practice with Python tutorials and quick quizzes to test your knowledge.

Requirements

No prerequisites required, but basic algebra and Python knowledge are a plus.

Description

This course takes you on a journey through the core principles of deep learning (DL), starting with the essential mathematical foundations that power these algorithms. From there, we dive into the limitations of traditional DL when faced with non-stationary or non-iid (independent and identically distributed) data. You’ll then be introduced to the exciting world of continual learning (CL) – also known as lifelong or incremental learning – exploring real-world scenarios and a variety of strategies used in cutting-edge CL research. We’ll deep dive into some of the most impactful and robust CL algorithms, followed by a captivating exploration of a novel CL application in controlling a soft robotic arm. With engaging videos, interactive presentations, detailed notes, hands-on tutorials, and quizzes after every section to evaluate the learned concepts, this course is designed to help you master the topic and their real-world applications. Whether you’re new to CL or looking to expand your knowledge in machine learning, this lecture series will help you gain practical insights and learn how to apply these algorithms to solve complex problems. By the end of the course, you’ll have a strong foundation in CL and its potential to transform industries like robotics, autonomous systems, real-time systems and beyond.

Overview

Section 1: Course overview

Lecture 1 Introduction

Section 2: Introduction to Deep Learning

Lecture 2 Part 1

Lecture 3 Part 2

Lecture 4 Coding Tutorial 1

Section 3: Introduction to Continual Learning

Lecture 5 Part 1

Lecture 6 Part 2

Section 4: Different Continual Learning Scenarios

Lecture 7 Part 1

Lecture 8 Part 2

Lecture 9 Coding Tutorial 2

Section 5: Introduction to Different Continual Learning Strategies and Evaluation Protocols

Lecture 10 Part 1

Lecture 11 Part 2

Lecture 12 Part 3

Section 6: Discussion on Regularization Algorithms in Continual Learning

Lecture 13 Lect 5

Lecture 14 Coding Tutorial 3

Section 7: Modular Architectures for Continual Learning

Lecture 15 Lect 6

Section 8: Hybrid Continual Learning Strategies and Robotic Applications

Lecture 16 Part 1

Lecture 17 Part 2

Ideal for ML researchers, roboticists, PhD students, or anyone looking to expand their expertise.