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Aug 2, 2024
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ML
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Easy Machine Learning guide!
This is a simplified machine learning guidebook written entirely by myself. The initial idea was to find a path to learn machine learning independently, as our school doesn’t offer any useful courses on the subject. Later, I realized I could write down what I’d learned as a blog for my friends and others to read, which also serves as a way to review the material myself.
The blog is primarily based on lectures from Stanford’s CS229 and Machine Learning by Zhi-Hua Zhou. I removed some outdated or irrelevant content and added insights from blogs I’ve read. I typed this all in Notion myself, so there might be some typo and so on, please let me know if you get one.
这里是简明机器学习系列博客。鉴于现在国内大多数本科课程大多无法应对大家就业和实际使用的需求,直接看《机器学习》或者《统计学习方法》又难免比较难以入门,特参考CS229和周,李两位老师的书写出了这个博客,希望你能喜欢。对于《机器学习》一书中部分现在看来过于冷门的算法,我几乎全部砍掉了,感觉实在用处不大。

Mathematic Preparations

Mathematic preparations

Supervised Learning

🫥
Linear regression
📔
Classification and logistic regression
🧞
Generalized linear models
🌲
Decision trees and Ensemble learning
🧬
Generative learning algorithm
🍩
Kernel method
🎰
Support vector machines

Deep Learning

🧠
Deep learning

Regulations and Generalization

🎟️
Bias-Variance Tradeoff and so more
📉
The double descent phenomenon and so more
®️
Regularization and model selection

Unsupervised Learning

🧩
Clustering algorithms
⚗️
EM algorithms
🍿
PCA and ICA

Reinforcement Learning

🦾
Reinforcement learning
🧀
LQR, DDP and LQG
📐
Policy Gradient (REINFORCE)

🧑‍💻
Practice code