Fall 2020

**Professor:** Bruce MacLeod

Room 222 Science
Bldg.

email : macleod at
maine.edu

Tel:
780-4285

Office Hours: M 12:45 PM - 1:30 PM

W 1:00 PM - 2:30 PM or by appointment

This class provides a practical
introduction to deep learning, including theoretical
motivations and how to implement it in practice. As part of
the course we will cover multilayer perceptrons,
backpropagation, automatic differentiation, and stochastic
gradient descent. After some background material, we start by
introducing convolutional networks for image processing,
starting from the simple LeNet to more recent architectures
such as ResNet. Secondly, we discuss sequence models and
recurrent networks, such as LSTMs, GRU, and the attention
mechanism. The goal of the course is to provide both a
good understanding and good ability to build modern
nonparametric estimators. The course loosely follows Dive
into Deep Learning in terms of notebooks, slides
and assignments

**Assignments:**

Assignments and a final project are
a fundamental part of this course. Two assignments will be group
projects which will involve a short (ten minute) presentation to
the class. The final project will involve ongoing consideration
throughout the semester and will be due on the day the final is
scheduled.

- Exams (2) 25%
- Assignments (4) 40%
- Final Project 15%
- Labs 10%

- Class Presentations/Writeups 10%

Any assignments handed in late will
incur a 5% a day reduction in the assignment grade. Any students
which choose to work together on an assignment must make a note
of the fact on the top of the assignment. Failure to do so will
result in no credit for the assignment and possible failure of
the course.

Links to related material

August/September

October

November

11/2 |
Modern Recurrent Neural Networks Reading : Finish Chapter 9 Lab : Modern Recurrent Networks Problem Set #3 Due |

11/4 |
Optimization Algorithms Reading : Chapter 11, Sections 11.1-11.3 |

11/9 | Optimization Algorithms Reading : Chapter 11, Sections 11.4-11.6 Graduate students/Extra Credit : finish Chapter 11, provide summary Lab: |

11/11 | Veterans Day |

11/16 |
Computer Vision Reading : Sections 13.1-13.4 Lab : |

11/18 |
Bias, Deployment issues Problem Set #4 Due |

11/23 | Second Exam |

11/25 |
Thanksgiving break |

December