- AI & ML Events – Discover the best upcoming hand-picked events in the field of artificial intelligence and machine learning
- Machine Learning – Stanford University This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Taught by: Andrew Ng
- MIT Artifical Intelligence Videos – MIT This course includes interactive demonstrations which are intended to stimulate interest and to help students gain intuition about how artificial intelligence methods work under a variety of circumstances.
- Machine Learning – Basic machine learning algorithms for supervised and unsupervised learning
- Deep Learning for Natural Language Processing – University of Oxford This is an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks.
- Tensorflow for Deep Learning Research –Stanford University This course will cover the fundamentals and contemporary usage of the Tensorflow library for deep learning research. We aim to help students understand the graphical computational model of Tensorflow.
- Deep Learning for Natural Language Processing –Stanford University Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc.
- Machine Learning – Cornell University This course will introduce you to technologies for building data-centric information systems on the World Wide Web, show the practical applications of such systems, and discuss their design and their social and policy context by examining cross-cutting issues such as citizen science, data journalism and open government. Course work involves lectures and readings as well as weekly homework assignments, and a semester-long project in which the students demonstrate their expertise in building data-centric Web information systems.
- Deep Learning Explained – Microsoft This course provides the level of detail needed to enable engineers / data scientists / technology managers to develop an intuitive understanding of the key concepts behind this game changing technology.
- Machine Learning: Regression – University of Washington In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,…). This is just one of the many places where regression can be applied.
- Machine Learning: Clustering & Retrieval – University of Washington A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover?
- Neural Networks for Machine Learning –University of Toronto with Geoffrey Hinton Learn about artificial neural networks and how they’re being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We’ll emphasize both the basic algorithms and the practical tricks needed to get them to work well.
- Machine Learning With Big Data –University of California, San Diego Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.
Artificial Intelligence
- Introduction to Artificial Intelligence –UC Berkeley This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm.
- Advanced Artificial Intelligence –Cornell University The design of systems that are among top 10 performers in the world (human, computer, or hybrid human-computer).
- Artificial Intelligence (AI) – Columbia University with Professor Ansaf Salleb-Aouissi This course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems.
Generative Adversarial Networks (GANs)
- A Beginner’s Guide to Generative Adversarial Networks (GANs) – Generative adversarial networks (GANs) are deep neural net architectures comprised of two nets, pitting one against the other.
- GAN — What is Generative Adversary Networks GAN? – GAN is about creating, like drawing a portrait or composing a symphony. This is hard compared to other deep learning fields..
Robotics
- Artificial Intelligence for Robotics – Georgia Tech Artificial Intelligence for Robotics by Sebastian Thrun
- Advanced Robotics –UC Berkeley The course introduces the math and algorithms underneath state-of-the-art robotic systems. The majority of these techniques are heavily based on probabilistic reasoning and optimization—two areas with wide applicability in modern Artificial Intelligence.
