1. Presentation and project

2. Presenter Rule of all: Be informative Be organize
Anticipate audience can learn something “new”
Should be easy to digest but not too trivial
Avoid significant overlap with prior presentations
Be organize
From high-level to detail
Simple demo will be great

3. Audience You are asked to rate each presenter
I will put the “task” up on canvas
It will be “graded” and a part of the 70%
Be critical. Treat yourself as a judge for some context. Need to comment on strength and weaknesses of a presentation
Grading metric for review (2 points max)
Below average (1 point)
Average (1.5 point)
Above average (2 points)
Your rating should be submitted before mid-night of the day after presentation
You have ~30 hours
Late submission won’t be accepted
Your input will be used to grade the presenter
Presenter should submit review also. It is treated as a self-appraisal and won’t be used to grade your presentation

4. Final Project Most important (40%) and lasting result Project types:
Start early and clearly define your task and dataset
Project types:
Apply existing neural network model to a new problem
Implement a complex architecture for old problem
Come up with a new neural network model
Theory

5. Procedures for Type 1-3 Define Task Define Dataset Define your metric
Example: summarization of text
Define Dataset
Search for public dataset first
Sometimes conference/competition will provide dataset
E.g., Document Understanding Conference (DUC)
Define your own (harder, need more new baselines)
Dataset from your own research
Clever use of public data
E.g., For text summarization, can use Wikipedia: intro paragraph and the rest of the article
Be creative, can try to look into blogs and news also for natural language processing
Define your metric
Search online for well established metrics on your task
Don’t invent your own arbitrarily. E.g., for text summarization, Rogue (Recall-oriented understudy for gisting evaluation) defines n-gram overlap to human summaries

6. Implement existing neural net model
Split your dataset
Training/validation/testing
Academic dataset often come pre-split
Don’t try to peek into your test set until the very last moment
Establish a baseline
Implement the simplest model first (for classification, logistic regression on some simple features?)
Compute metrics on training and validation sets
Analyze errors If metrics are amazing and no errors: done, problem was too easy, restart :)
Implement existing neural net model
Compute metric on training and validation sets
Analyze output and errors
Minimum bar for this class (guarantee half of the project score)

7. Try out different model variants
CNN/RNN/Hybrid?
Depth/width variation?
ReLU/tanh/leaky ReLU/etc.?
Regularization tricks? Gradient flow improvement? …
Some tips and suggestions:
Always be close to your data
Try to visualize the dataset collect summary statistics
Inspect the error events
Analyze how different hyperparameters affect performance

8. Project proposal (Due on 3/14)
Define problem
Tentative approach(s)
Names of group members
How tasks will be split (if group project)
Work on the project now if you can. Don’t wait until after submitting your proposal. If you are not sure whether your project idea is good, just drop me an

9. Grading metric (40%) Presentation: (10 out of 40)
clarity, structure, references
Informative: e.g., background literature survey
good insights and discussions of methodology, analysis, results, etc.
Technical: (15 out of 40)
Correctness, innovation, depth (ad hoc/theoretical-based)
Evaluation and results: (15 out of 40)
sound evaluation metric
thoroughness in analysis and experimentation
results and performance
Labor cost adjustment
No of members 1 2 3 4 5 6
7>
Deduction (in %) 10 20 40 80

10. Submission (due on 5/9)
minutes screencast/video “presentation” explaining what you have done
Longer video is not always better
Should include
Problem description and motivation
What did you try to solve
Can be shorter if it is a common problem (e.g., segmentation)
Your approach(s)
Evaluation
Discussion: why it works or doesn’t work
Written report: not mandatory. But maximum 20% extra credit
5 times late penalty (25% per day)

11. Expectation Quality Grade Just submitting 1/4
Department-level competition
2/4
University-level competition
3/4
National-level competition
4/4

12. Final grade A: ~ 85% or above B: 70%-85% C: 55%-70% D: 40%-55%
F: Below 40%