Feature Extraction:

• Hand-crafted features: SIFT, HOG, LBP
• Feature engineering and selection

Classification Algorithms:

• k-Nearest Neighbors (kNN)
• Support Vector Machines (SVM)
• Decision Trees and Random Forests
• Case Study: Building an image classifier using the techniques above (e.g., handwritten digit recognition)

The limitations of traditional approaches

• Biological inspiration of neural networks
• Building blocks of CNNs:
• Convolutional layers
• Pooling layers
• Activation functions (ReLU, sigmoid)
• Fully connected layers

Training CNNs:

• Loss functions (cross-entropy)
• Optimization algorithms (stochastic gradient descent, Adam)
• Backpropagation