CSC 4611 - Introduction to Deep Learning

• Understand the process of backpropagation and its role in training deep networks
• Implement a basic deep learning network library
• Apply deep learning to a unique dataset and analyze the performance
• Compare options for training deep neural networks, for example, optimization methods, generalization methods, normalization, initialization methods
• Apply transfer learning to leverage pre-trained models
• Compare various modern network architectures, for example, convolutional neural networks (CNNs), encoder-decoders, generative adversarial networks (GANs), or transformers. Emphasis on topics can vary depending on the instructor's specialties
• Explain the convolution operation and its significance for feature extraction in computer vision applications, particularly in the context of convolutional neural networks (CNNs).
• Evaluate training methods and alternative architectures based on model accuracy, constraints, and training performance
• Discuss the ethical implications of deep neural network applications

• An ability to train a machine learning model on a provided dataset using a framework such as Keras or Pytorch
• An ability to assess the quality of a machine learning model
• Understand the concepts and application of supervised and unsupervised learning
• Understand the role of optimization in machine learning
• Understand overfitting
• Be familiar with basic linear algebra such as matrix multiplication
• Be familiar with multivariate calculus such as partial derivatives