Description
Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition.
You'll learn:
- Linear/Logistic regression
- Decision Trees
- KNN
- Support Vector Machines
- Neural Networks
- Naive Bayes
- Gradient Descent
Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai
Lien de la playlist YouTube : https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU
