Important Objectives of Machine Learning:

As per ML theory (combines CS, stats, optimization), main objectives:

1. Prediction / Generalization: Learn model $f$ that generalizes to unseen data, minimizing expected risk $R(f)=E[L(f(x),y)]$, not just training error. Overcome overfitting via bias-variance trade-off.

2. Knowledge Discovery / Pattern Recognition: Extract hidden patterns, associations, trends from large data (data mining).

3. Automation & Decision Making: Automate tasks requiring human intelligence (classification, recommendation, control) with minimal human intervention.

4. Handling Uncertainty & Noise: Deal with incomplete, noisy, high-dimensional data via probabilistic modeling.

5. Adaptation: Continuously improve with more experience $E$ (online learning, concept drift).

6. Optimization: Find $w^* = \arg\min_w J(w)$ efficiently (GD, SGD).

7. Efficiency & Scalability: Handle big data with feasible computation/memory.

8. Interpretability vs Performance: Balance accurate black-box (deep nets) vs explainable models.

9. Specific statistical goals: Minimize empirical risk + regularization: $J(w)=\frac1n\sum L(y_i, f(x_i;w)) + \lambda\Omega(w)$

In short: build systems that learn from data to make accurate, robust, scalable predictions/decisions and discover useful knowledge.