Spring 2026 Assignments
Module 0: Preparation
Module 1: Neural Networks
Image Classification: Data-driven Approach, k-Nearest Neighbor, train/val/test splits
L1/L2 distances, hyperparameter search, cross-validation
Linear classification: Support Vector Machine, Softmax
parameteric approach, bias trick, hinge loss, cross-entropy loss, L2 regularization, web demo
Optimization: Stochastic Gradient Descent
optimization landscapes, local search, learning rate, analytic/numerical gradient
chain rule interpretation, real-valued circuits, patterns in gradient flow
Neural Networks Part 1: Setting up the Architecture
model of a biological neuron, activation functions, neural net architecture, representational power
Neural Networks Part 2: Setting up the Data and the Loss
preprocessing, weight initialization, batch normalization, regularization (L2/dropout), loss functions
Neural Networks Part 3: Learning and Evaluation
gradient checks, sanity checks, babysitting the learning process, momentum (+nesterov), second-order methods, Adagrad/RMSprop, hyperparameter optimization, model ensembles
Putting it together: Minimal Neural Network Case Study
minimal 2D toy data example
Module 2: Convolutional Neural Networks
Convolutional Neural Networks: Architectures, Convolution / Pooling Layers
layers, spatial arrangement, layer patterns, layer sizing patterns, AlexNet/ZFNet/VGGNet case studies, computational considerations
Understanding and Visualizing Convolutional Neural Networks
tSNE embeddings, deconvnets, data gradients, fooling ConvNets, human comparisons
Student-Contributed Posts