Schedule (Spring 2025)
Lectures
Disclaimer: All lecture materials are available on the course GitHub repository. The majority of the materials are borrowed from other (fantastic) courses, specifically, from CMU 11-785, Stanford CS231n, and Stanford CS224n.
Fundamentals
- Introduction to CSC 561 and Course Logistics
- Perceptron
- MLPs: Representation
- MLPs: Learning, Empirical Risk Minimization
- Gradient Descent, Backpropagation
- Vectorized Backpropagation, Computational Graphs
- Optimization (Second Order Methods, RProp, Momentum)
- Optimization (Mini-Batches, RMSProp, Adam)
- Regularization, Activation Functions, Weights Initialization, Dropout, Data Augmentation, Hyperparameter Tuning
Vision
- Image Filtering, Convolutions
- Convolutional Neural Networks (CNNs), Batch Normalization
- CNN Architectures (AlexNet, VGG, ResNet)
- Image Segmentation, Transposed Convolutions
- Object Detection (R-CNN, Fast R-CNN, Faster R-CNN, YOLO)
- Vision Transformers (ViT)
Language
- Word embeddings, Word2Vec, n-Gram Language Models
- RNNs, LSTMs
- Encoder/Decoder architectures, Machine Translation, Attention
- Self-Attention, Transformers (encoders and decoders)
- Transformers (BERT, T5, GPT), Pre-training, and Fine-tuning
Assignments
- Quiz 1 (Understanding deep learning requires rethinking generalization), date 02/18
- Assignment 1 (MLPs, Batch Gradient Descent), due 03/05 11:59pm
- Quiz 2 (A ConvNet for the 2020s), date 03/27
- Assignment 2 (Image Classification with CNNs, Image Caption Generation with RNNs), due 04/18 11:59pm
- Quiz 3 (Masked Autoencoders are Scalable Vision Learners), date 04/29
Project
- Deliverables, due 05/05 11:59pm via Gradescope
- Final Report (PDF)
- Presentation - Slide deck (PDF)
- Presentations (15 mins each team), 05/06 2p at TBA