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Video
Interested in harnessing the power of Generative AI (GenAI) for your studies? Join us in exploring the GenAI platform, its functionality and usage policies in our upcoming workshop. Learn about how GenAI can enhance your learning experience and how to employ it in your studies while maintaining data privacy and security. We'll introduce you to 'prompts engineering' and emphasise the importance of academic integrity in the context of AI technology usage. Come and join this workshop co-organised by EDC and ITS.
Event Date: 27/9/2023
Facilitator(s): Chan, Dick (EDC), Mark, Kai Pan (EDC), Tam, Barbara (EDC), Leung, Rian (ITS)
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Video
Curious about integrating Generative AI (GenAI) into your teaching methodologies? Embark on a journey with EDC and ITS in a comprehensive workshop introducing the innovative GenAI platform. This session will guide you through the platform's operations, explaining its usage policies. During the workshop, we'll briefly discuss the need for redesigning our assessment strategies in sync with this advanced tool to optimise learning outcomes effectively. Even more importantly, we will discuss data security and privacy concerns surrounding GenAI usage. This workshop offers an unrivalled opportunity to expand your understanding and proficiency in using AI in an educational context. If you're prepared to explore the cutting edge of education technology, then this is the ideal workshop for you.
Event Date: 20/9/2023
Facilitator(s): Chan, Dick (EDC), Mark, Kai Pan (EDC), Tam, Barbara (EDC), Leung, Rian (ITS)
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Others
Kaggle offers a no-setup, customizable, Jupyter Notebooks environment. Access GPUs at no cost to you and a huge repository of community published data & code. Inside Kaggle you’ll find all the code & data you need to do your data science work. Use over 50,000 public datasets and 400,000 public notebooks to conquer any analysis in no time.
- Subjects:
- Computing
- Keywords:
- Machine learning Artificial intelligence Big data
- Resource Type:
- Others
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Video
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
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MOOC
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
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e-book
Machine learning techniques have the potential of alleviating the complexity of knowledge acquisition. This book presents today’s state and development tendencies of machine learning. It is a multi-author book. Taking into account the large amount of knowledge about machine learning and practice presented in the book, it is divided into three major parts: Introduction, Machine Learning Theory and Applications. Part I focuses on the introduction to machine learning. The author also attempts to promote a new design of thinking machines and development philosophy. Considering the growing complexity and serious difficulties of information processing in machine learning, in Part II of the book, the theoretical foundations of machine learning are considered, and they mainly include self-organizing maps (SOMs), clustering, artificial neural networks, nonlinear control, fuzzy system and knowledge-based system (KBS). Part III contains selected applications of various machine learning approaches, from flight delays, network intrusion, immune system, ship design to CT and RNA target prediction. The book will be of interest to industrial engineers and scientists as well as academics who wish to pursue machine learning. The book is intended for both graduate and postgraduate students in fields such as computer science, cybernetics, system sciences, engineering, statistics, and social sciences, and as a reference for software professionals and practitioners.
- Course related:
- COMP4432 Machine Learning
- Subjects:
- Computing
- Keywords:
- Machine learning
- Resource Type:
- e-book
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Others
Extract human-understandable insights from any machine learning model.
- Subjects:
- Computing
- Keywords:
- Python (Computer program language) Machine learning
- Resource Type:
- Others
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Others
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).
- Course related:
- COMP4432 Machine Learning
- Subjects:
- Computing
- Keywords:
- Machine learning
- Resource Type:
- Others
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MOOC
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.
This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.
It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
- Course related:
- AAE5103 Artificial Intelligence in Aviation Industry
- Subjects:
- Computing
- Keywords:
- Machine learning Artificial intelligence
- Resource Type:
- MOOC
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MOOC
This course covers a wide variety of topics in machine learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve the data science problems found in practice. This course also serves as a foundation on which more specialized courses and further independent study can build. This course was designed as part of the core curriculum for the Center for Data Science's Masters degree in Data Science. Other interested students who satisfy the prerequisites are welcome to take the class as well. Note that class is intended as a continuation of DS-GA-1001 Intro to Data Science, which covers some important, fundamental data science topics that may not be explicitly covered in this DS-GA class (e.g. data cleaning, cross-validation, and sampling bias).
- Course related:
- LGT6801 Guided Study in Logistics I
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Big data Data mining Machine learning Mathematical statistics -- Data processing
- Resource Type:
- MOOC