Open Educational Resources | Pao Yue-kong Library, The Hong Kong Polytechnic University

Video

Lecture 2 - Linear Regression and Gradient Descent | Stanford CS229: Machine Learning (Autumn 2018)

This video is take an adapted version of this course as part of the Stanford Artificial Intelligence Professional Program.

Course related:: AP619 Microfabrication Laboratory
Subjects:: Computing
Keywords:: Machine learning Computer algorithms
Resource Type:: Video

Video

But what is a Neural Network? | Deep learning, chapter 1

This video introduces the basic concept and the overall working process of the ANN models, especially how the backpropogation works. How to establish a deep neuro network.

Course related:: LGT6801 Guided Study in Logistics I
Subjects:: Computing
Keywords:: Machine learning Neural networks (Computer science)
Resource Type:: Video

MOOC

Intro to Machine Learning

Learn the core ideas in machine learning, and build your first models.

Course related:: ENG2002 Computer Programming
Subjects:: Computing
Keywords:: Machine learning
Resource Type:: MOOC

MOOC

The Elements of AI is a series of free online courses created by Reaktor and the University of Helsinki. We want to encourage as broad a group of people as possible to learn what AI is, what can (and can’t) be done with AI, and how to start creating AI methods. The courses combine theory with practical exercises and can be completed at your own pace.

Course related:: COMP4431 Artificial Intelligence
Subjects:: Computing
Keywords:: Artificial intelligence Machine learning
Resource Type:: MOOC

MOOC

Machine Learning

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Course related:: EIE6207 Theoretical Fundamental and Engineering Approaches for Intelligent Signal and. Information Processing and COMP4434 Big Data Analytics
Subjects:: Computing
Keywords:: Artificial intelligence Machine learning
Resource Type:: MOOC

Video

Natural Language Processing (NLP) Zero to Hero

Welcome to Zero to Hero for Natural Language Processing using TensorFlow! If you’re not an expert on AI or ML, don’t worry -- we’re taking the concepts of NLP and teaching them from first principles with our host Laurence Moroney.

Subjects:: Computing
Keywords:: Machine learning Natural language processing (Computer science)
Resource Type:: Video

Others

Machine Learning Foundations and Machine Learning Techniques MOOC

I am fortunate to be among the very first NTU EECS professors to offer two Mandarin-teaching MOOCs (massive open online courses) on NTU@Coursera. The two MOOCs are Machine Learning Foundations (Mathematical, Algorithmic) and Machine Learning Techniques and are based on the textbook Learning from Data: A Short Course that I co-authored. The book is consistently among the best sellers in Machine Learning on Amazon.
The slides of the MOOCs below are available as is with no explicit or implied warranties. The slides themselves are shared by CC-BY-NC 3.0, but the copyright of all materials (figures in particular) remain with the original copyright holder (in almost all cases the authors of the Learning from Data: A Short Course book).