CSC483: Introduction to Deep Learning

Personnel

Course Description

This course covers the foundations of deep learning, including fundamental neural network architectures (e.g., multilayer perceptrons) and training methodologies, including widely used optimization techniques (e.g., momentum, RMSprop, Adam). It also addresses generalization and regularization strategies (e.g., overparameterization, the double descent phenomenon, and weight decay). We will explore cutting-edge neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers (e.g., GPT and BERT) with attention mechanisms. Students will gain hands-on experience by implementing these models and applying them to real-world problems in computer vision (CV), natural language processing (NLP), and computational biology. This course covers the foundations of deep learning, including fundamental neural network architectures (e.g., multilayer perceptrons) and training methodologies, including advanced optimization techniques (e.g., momentum, RMSprop, Adam). It also addresses generalization and regularization strategies (e.g., overparameterization, the double descent phenomenon, and weight decay). We will explore cutting-edge neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers (e.g., GPT and BERT), and graph neural networks (GNNs). Students will gain hands-on experience by implementing these models and applying them to real-world problems in computer vision, natural language processing, and graph machine learning.

Prerequisites

Textbook

No textbook is required. Materials will be drawn from classical books and recent papers. Recommended readings:

A list of key papers in deep learning will also be provided.

Grading

Schedule

Additional Reading and Resources

Assignments