Understand the foundational mathematics and key concepts driving neural networks and machine learning.

Analyze and apply machine learning algorithms, optimization methods, and loss functions to train and evaluate models effectively.

Explore the design and structure of feedforward neural networks, using gradient descent to optimize and train deep models.

Investigate convolutional neural networks, their elements, and how they apply to real-world problems like image processing and computer vision.