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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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Video
In this lecture, we consider strategies for adversarial games such as chess. We discuss the minimax algorithm, and how alpha-beta pruning improves its efficiency. We then examine progressive deepening, which ensures that some answer is always available.
- Course related:
- COMP4431 Artificial Intelligence
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Artificial intelligence
- Resource Type:
- Video
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Video
An example of using least squares to do data fitting. In this example, we demonstrate how to 1) fit a straight line using ordinary least-squares method, and 2) estimate value of a new input based on the fitted line.
- Course related:
- LSGI3242A Digital Terrain Modelling
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Least squares Regression analysis
- Resource Type:
- Video
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Video
Statistics, Machine Learning and Data Science can sometimes seem like very scary topics, but since each technique is really just a combination of small and simple steps, they are actually quite simple. My goal with StatQuest is to break down the major methodologies into easy to understand pieces. That said, I don't dumb down the material. Instead, I build up your understanding so that you are smarter.
- Course related:
- HTI34016 Introduction to Clinical Research
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Statistics Mathematical analysis Data mining Machine learning
- Resource Type:
- Video
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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