Explain the theoretical foundations of machine learning from both statistical and computational perspectives. Discuss bias-variance tradeoff and its implications for model performance. Elaborate on the concepts of overfitting, underfitting, and regularization. Explain the Probably Approximately Correct (PAC) learning framework and VC dimension. Discuss the No Free Lunch theorem and its significance. Analyze the theoretical guarantees of different learning approaches including supervised, unsupervised, and reinforcement learning.