1. Theory and Applications
Deep Learning MSiA
Theory and Applications

2. Presentations from 4-6 PM ~120 minutes for 8 groups
[Logistics]
Last class 4-7 PM
Presentations from 4-6 PM ~120 minutes for 8 groups
Each group on stage for 15 minutes
12 minutes for presentations (max ~8 slides)
3 minutes for 1-2 questions
Lightning pitches for Data Viz 6-7 PM
Poster session 7-8 PM

3. Project short description Group members
[Project Name]
Project title
Project short description
Group members 1 2 3 4

4. Statement of the problem, why it matters and to whom
Problem Statement
Statement of the problem, why it matters and to whom
Explain why the problem is hard to solve, and why others haven’t adequately tackled this problem. Describe briefly the approaches already taken to solve the problem.

5. Dataset Describe your dataset Show some example data points
Explain how you cleaned the data
Note challenges in working with the data
Explain how much computation power was required to process the data
If applicable, explain how you augmented the data, e.g. shifting data, flipping, changing colors, etc
Was the dataset big enough, do you think overfitting is likely?
Show loss curve during training to see how well the network has learned

6. Describe the technical approach to your problem
Use diagrams to illustrate workflow
Justify why your approach is reasonable compared to alternate approaches
Input
Hidden L1
Hidden L2
Output
𝑊 3 …
𝐿 2
𝐿 3
𝑊 2
𝐿 1
𝑊 1

7. Additional slide on approach
Technical approach 2
Additional slide on approach

8. Include results based on your experiments Result 2 Result 3

9. Additional slide on results

10. Brief summary of what you discovered based on results
Conclusion
Brief summary of what you discovered based on results
Limitations of approach
How to improve/future work

11. Include print and electronic sources in alphabetical order
References
Include print and electronic sources in alphabetical order