Machine Learning CS229
About The Course
Course Description: This course provides a broad introduction to machine learning and statistical pattern recognition. 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.
- Name: CS229 Machine Learning
- Offered By: Stanford University (Youtube)
- Lecturer: Andrew NG
- Course website: https://cs229.stanford.edu/
What I’ve Learned
Through CS229 Machine Learning at Stanford University, I’ve gained a strong theoretical foundation in machine learning and statistical pattern recognition. I’ve studied key concepts in supervised learning, including generative and discriminative models, neural networks, and support vector machines, as well as unsupervised learning methods like clustering, dimensionality reduction, and kernel techniques.
The course deepened my understanding of learning theory, including the bias-variance tradeoff, and provided a solid grounding in reinforcement learning. Although theoretical in nature, these concepts have given me a robust framework to approach complex machine learning problems and critically analyze various models and methods in the field.