2020 Semester Two (November-December 2020)
Examination Period
Faculty of Information Technology
EXAM CODES: FIT5215
TITLE OF PAPER: Deep learning
EXAM DURATION: 2 hours 20 mins
Rules
During an exam, you must not have in your possession any item/material that has not been authorised for your exam. This includes
books, notes, paper, electronic device/s, mobile phone, smart watch/device, calculator, pencil case, or writing on any part of your
body. Any authorised items are listed above. Items/materials on your desk, chair, in your clothing or otherwise on your person will
be deemed to be in your possession.
You must not retain, copy, memorise or note down any exam content for personal use or to share with any other person by any
means following your exam.
You must comply with any instructions given to you by an exam supervisor.
As a student, and under Monash University’s Student Academic Integrity procedure, you must undertake your in-semester tasks,
and end-of-semester tasks, including exams, with honesty and integrity. In exams, you must not allow anyone else to do work for
you and you must not do any work for others. You must not contact, or attempt to contact, another person in an attempt to gain
unfair advantage during your exam session. Assessors may take reasonable steps to check that your work displays the expected
standards of academic integrity.
Failure to comply with the above instructions, or attempting to cheat or cheating in an exam may constitute a breach of instructions
under regulation 23 of the Monash University (Academic Board) Regulations or may constitute an act of academic misconduct
under Part 7 of the Monash University (Council) Regulations.
Authorised Materials
CALCULATORS YES NO Calculator
DICTIONARIES YES NO
NOTES YES NO Notes – Double Sided A4 x 1 -Handwritten only
PERMITTED ITEM YES NO
if yes, items permitted are:
Page 1 of 17
Instructions
Once your exam finishes, you will be given time to scan
a QR code and upload your answers using
your smartphone and laptop.
Here’s how to do it.
This examination is designed for FIT5215 Deep Learning unit, Semester 2, 2020. It contains THREE (3) parts with a total of 100
marks:
Part A contains 11 multiple-choice questions, together they are worth a total of 25 marks. Questions with m
ore than one answer,
a correct choice will receive a partial mark and incorrect choices will reduce the mark. To receive full marks, only correct
choices must be selected.
Part B contains 8 short workout questions, worth 35 marks. These questions typically require short knowledge answers and
calculations based on the knowledge you have learned from the unit. Having the calculator handy for these questions is
recommended.
Part C contains 9 mixed and written-answer questions, worth 40 marks. These questions typically assess the knowledge and
understanding of lecture contents.
Good luck with your exam!
Page 2 of 17
Instructions
Information
This examination is designed for FIT5215 Deep Learning unit, Semester 2, 2020. It contains THREE (3)
parts with a total of 100 marks:
Part A contains 11 multiple-choice questions, together they are worth a total of 25 marks.
Questions with more than one answer, a correct choice will receive a partial mark and incorrect
choices will reduce the mark. To receive full marks, only correct choices must be selected.
Part B contains 8 short workout questions, worth 35 marks. These questions typically require short
knowledge answers and calculations based on the knowledge you have learned from the unit.
Having the calculator handy for these questions is recommended.
Part C contains 9 mixed and written-answer questions, worth 40 marks. These questions typically
assess the knowledge and understanding of lecture contents.
Good luck with your exam!
Page 3 of 17
Part A – Multiple Choices (11 questions, 25 marks)
Question 1
Given an auto-encoder with the encoder and the decoder , match the correct type of auto-
encoders and their corresponding objective functions:
Objective function A: .
Objective function B: .
Objective function C: .
Standard auto-encoder
Objective function A Objective function B
Objective function C
Denoising auto-encoder
Objective function A Objective function B
Objective function C
Sparse auto-encoder
Objective function A Objective function B
Objective function C
3
Marks
gα hβ
[d(x, ( (x))) + λΩ(z)]minα,β Ex～P hβ gα
[d(x, ( (x)))]minα,β Ex～P hβ gα
[ [d(x, ( (x + )))]]minα,β Ex～P E ～N(0,ηI) hβ gα
Question 2
Let and , what is the correct formula for the gradient vector