Email: p.hand@northeastern.edu Office Hours: Fridays 4-5 PM and by appointment

Email: cocola.j@northeastern.edu Office Hours: Tuesdays 1-2 PM and by appointment

Email: gunn.s@northeastern.edu Office Hours: Thursdays 12-1 PM and by appointment

Email: srivastava.anm@northeastern.edu

Introduction to deep learning, including the statistical learning framework, empirical risk minimization, loss function selection, fully connected layers, convolutional layers, pooling layers, batch normalization, multi-layer perceptrons, convolutional neural networks, autoencoders, U-nets, residual networks, gradient descent, stochastic gradient descent, backpropagation, autograd, visualization of neural network features, robustness and adversarial examples, interpretability, continual learning, and applications in computer vision and natural language processing. Assumes students already have a basic knowledge of machine learning, optimization, linear algebra, and statistics.

- be able to train neural networks at a variety of tasks
- be able to select appropriate neural network architectures for a variety of tasks
- have read and be able to discuss the contents and contributions of important papers in the field
- have a basic theoretical understanding of the tools of deep learning

- Each week, students will be given a set of questions to answer in writing about that week's content. Students are expected to submit the answers to these questions to Gradescope by 2:30 PM on the relevant day of class in order to ensure they are prepared to engage in the class discussion. Students will work in small groups.
- Students are expected to participate in the whole-class discussions by presenting prepared responses and answering spontaneous questions.
- If a student is unable to participate in a class, for example due to an absence, then they are expected to submit to Gradescope a typed or handwritten summary of the course discussion from that day. This is not intended to be a transcript of every word that was said; it is meant to concisely convey all of the important ideas that were discussed.
- There will be three homework assignments throughout the semester. They will involve both pencil-and-paper work and computation involving training neural networks. The results will be submitted to gradescope in the form of a pdf.
- Students will complete a project. They will reproduce an empirical observation in a paper of their choice by independently recreating and executing code to train and test that network. You may not use the code from the specific paper you are replicating, but you may use code from other papers.

Letter grades will be assigned on the following scale: 93%+ A, 90-92% A-, 87-89 B+, 83-87% B, 80-82% B-, 77-79 C+, 73-77% C, 70-72% C-,60-70% D, 0-59% F.