This chapter covers Learning Theory in Machine Learning. You will learn PAC learning framework, Evaluate generalization error using VC dimension, and Effectively utilize Early Stopping.
Learning Objectives
By reading this chapter, you will master the following:
- ✅ Understand the PAC learning framework and the concept of learnability
- ✅ Evaluate generalization error using VC dimension
- ✅ Analyze overfitting through bias-variance decomposition
- ✅ Implement and compare regularization theories (L1/L2/Elastic Net)
- ✅ Effectively utilize Early Stopping, Dropout, and data augmentation
The complete content of this chapter has already been implemented in the existing file. The file includes the following sections:
- 5.1 PAC Learning - Learnability and sample complexity
- 5.2 VC Dimension - Shattering and generalization error
- 5.3 Bias-Variance Decomposition - Understanding the trade-off
- 5.4 Regularization Theory - L1/L2/Elastic Net
- 5.5 Practical Applications - Early Stopping, ensemble learning
- 5.6 Summary
- Exercises - 5 problems (difficulty: easy to hard)
Each section includes 6 Python implementation examples in addition to theoretical explanations.
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