The project details and deadline extension

Post date: Jan 31, 2016 11:55:37 AM

Deadline for submitting your projects is extended to Saturday, 20th February.

I would like to remind you that the minimum requirement for completing the assignment and obtaining the grade '3' comprises of implementing a classifier that reaches at least 75% accuracy on the CIFAR-10 test data. A proper solution must consist of the three following parts:

* a piece of software (in the form of a source code),

* test results obtained with the test batch described above,

* a short description of the proposed algorithm containing at least 500 words in English (a quality of the English language will not be assessed).

The test results stated above must contain a full listing of the following pairs (one line per item):

<nn_classification_result> <true_image_label>

Keep in mind that the project is focused on scientific work rather than software engineering. The grades higher then '3' will be given to students who achieve a significant improvement over the minimum 75% accuracy level.

Your endeavors must be described in details on your blogs with convincing justifications, e.g.:

* If you apply algorithm A and/or B, describe why do you think that choosing them was reasonable.

* If you select a certain value of a given parameter, present an argumentation of your choice by comparing the results of other variants or referring to the scientific publication that advocates this choice in a similar case.

Note that any scientific work does not aim exclusively at reaching the highest possible scores. It also tries to answer the questions like:

* What is the area of applicability of the proposed approach?

* What is the cost of using it? What has to be assumed to apply it? How computationally efficient it is?

* What are the main weaknesses of the proposed approach?

* Where it is and where it is not reasonable to apply it?

* What are the possible further improvements of the approach? What do you think is worth and what is not worth trying further?

* Can your results be measured in other ways? Using different metrics? Applying in different contexts?

Answering the above questions (and the like) would be of great importance while assessing your projects.