DeepLearning_MSDS21

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DeepLearning_MSDS21

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The repository associated with MSDS631 at University of San Francisco in Summer 2021.

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# MSDS 631: Deep Learning Neural Networks
The repository associated with MSDS631 at University of San Francisco in Summer 2021. This course has ended; solutions to the assignments are available upon request.

**Instructor:** Michael Ruddy

**Email**: mruddy@usfca.edu

**Class Time**: Tuesdays/Thursdays at 10am - 12pm Pacific Time

**Location:** Zoom (see Canvas)

**Office Hours**: Tuesdays/Thursdays at 3pm - 4pm Pacific Time

**Syllabus**: [Link](https://github.com/mgruddy/DeepLearning_MSDS21/blob/main/MSDS631_DeepLearning_Syllabus.pdf)

## Course Learning Outcomes

- Understand Neural Networks and basic techniques for using them effectively (dropout, batch normalization, data augmentation, etc.).
- Learn how to handle image data and use Convolutional Neural Networks for various imaging tasks.
- Learn how to handle text data and use Recurrent Neural Networks and Transformers for various Natural Language Processing tasks and for other tasks involving sequential data.
- Practice implementing these techniques from scratch using PyTorch
- Complete a Deep Learning project as part of team from start to end using real world data:
  - Research literature for the problem,
  - Prepare/clean data,
  - Apply the appropriate deep learning techniques,
  - Evaluate and communicate the results.

## Course Overview

### Assessment

The course will be graded based on the following components:

- **Professionalism** (20%): Students will attend each live lecture (unless given prior permission for watching recording), participating in all quizzes/polls, and will be ready to respond if called upon. Additionally students will follow the code of conduct.
- **Assignments** (30%): There will be 3-4 Homeworks and 1-2 live Tests/Quizzes. These will be graded for correctness. For homeworks, if it is turned in late, there is a 10% score reduction for each day after the due date.
- **Labs** (20%): There will be many Lab assignments which will be graded for completion. Completion means each problem has been reasonably attempted or the student indicates *in detail* why they confused or unable to complete it. Labs turned in late receive half credit if they are received within a week of the due date.
- **Final Project** (30%): Students will complete a final project that includes two presentations and a GitHub repository (each worth 10% of their total grade). Students will work in teams of two to complete their final project.

### Code of Conduct

As students will often be asked to share and discuss their work, and work on assignments with their peers, the code of conduct **MUST** be followed. Students are expected to be highly respectful of their peers during and outside of class to help foster an inclusive environment where everyone is comfortable making mistakes and contributing. When working as a group, it is every group member's responsibility to make sure everyone has input and understands the answers given. Students are also expected to be respectful of the instructor and other MSDS faculty.

## Schedule

**Week 1**

HW1: [Recommendation Systems with a Feed Forward Neural Network](https://github.com/mgruddy/DeepLearning_MSDS21/blob/main/Assignments/Homework_1/Homework1.pdf) (Due 7/16 at 11:59pm Pacific Time)

**Week 2**

**Week 3**

**Week 4**

**Week 5**

HW3: [Bounding Boxes](https://github.com/mgruddy/DeepLearning_MSDS21/blob/main/Assignments/Homework_3/Homework3.pdf) (Due 8/10 at 11:59pm Pacific Time)

**Week 6**

| Topic | Slides | In Class Code | Lab | Due Date |
 | :---  | :---:  | :---:  | :---:  | :---: |
 | 8/10: More Topics: Style Transfer, GANs, Reinforcement Learning| [Slides](https://github.com/mgruddy/DeepLearning_MSDS21/blob/main/Slides/Lecture9_More_Topics.pdf) | | | |
 | 8/12: Final Project Presentation| | | | |
 
 The final projects produced by the students in this course can be viewed [here](https://docs.google.com/spreadsheets/d/1kUJCgP5zK7Mg0FWoD59JZxE04A1s6AfPNAjaomQSHP0/edit?usp=sharing).

### Important USF Dates
 
 - Tuesday, July 6th: First day of classes
 - Monday, July 12th: Census Day (Last day to withdraw with tuition reversal)
 - Thursday, August 12th: Last day of class
 
 ### Important Class Dates

- Thursday, July 15th: Final Project Groups must be finalized
 - Tuesday, July 20th: Final Project Description Presentation Date
 - Thursday, August 12th: Final Project Presentation Date

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