ECE101 Fall 2024 Exam 2

Study Guide

Question
1
(Lecture: Search Engines)

Explain in your own words how Search Engines work?

Sample Solution
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Question
2
(Lecture: Search Engines)

List the different types of information about the user that are typically available to the search engine?

Sample Solution
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Question
3
(Lecture: Search Engines)

List the advantages and disadvantages of tracking by search engines from the perspective of the user

Sample Solution
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Question
4
(Lecture: Search Engines)

What are some ways legislation can help with protecting the privacy of internet service users?

Sample Solution
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Question
5
(Lecture: Search Engines)

Explain the binary search process using any example you like.

Sample Solution
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Question
6
(Lecture: Search Engines)

1
.
Mention a few possible ways web pages could be ranked by a search engine.
2
.
Explain in your own words the intuition behind “page rank” being a measure of importance of a web page.

Sample Solution
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Question
7
(Lecture: Search Engines)

Consider the example mentioned in class while explaining the intuition for Page Rank. There is a person at every node on the web graph. Each person chooses an outgoing link at random(equal probability, independently of past/future decisions) and walks to another node. The process is repeated many times, then stopped to count how many people are at a node to find the node’s “importance” (rank). The higher the number of people at a node, the greater the rank of the page. (Page Rank is the expected number of “people” at a node
in a web graph).
​
If at any point, a person ends up at a page with no outgoing link (some pages may have no hyperlinks at all on them), they can ‘start over’ by choosing a new node at random instead and go there.
​
Using this example, which node in the following graph would have the highest page rank? Briefly explain why?
In[]:=
g=
;

Sample Solution
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Question
8
(Recommendation Engines)

List some companies in the present day that care about building efficient and successful recommendation engines. Explain how they might use the recommendation engines.

Sample solution
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Question
9
(Recommendation Engines)

Explain how the following data may be represented in feature space:
In[]:=
Pokémon
POKÉMON
["Properties"]
Out[]=

hidden abilities
,
natural abilities
,
attack
,
base experience yield
,
base friendship
,
catch rate
,
defense
,
effort value yield
,
egg cycle
,
egg groups
,
entity classes
,
entity type list
,
evolves from
,
evolves into
,
experience type
,
footprint
,
form
,
gender
,
proportion of females
,
proportion of males
,
generation
,
height
,
hit points
,
icon
,
image
,
Japanese characters
,
Japanese transliteration
,
name
,
Pokédex number
,
Pokédex color
,
related food type
,
shape
,
special attack
,
special defense
,
species
,
speed
,
total stats
,
type
,
weight

◼
  • Pokemon
    • ◼
  • Cities
    • ◼
  • Countries
    • ◼
  • Buildings on UIUC campus
    • ◼
  • Courses offered by ECE
    • ◼
  • Pixels in a digital image
    • ◼
  • Colors
    • ◼
  • Research publications
    • ◼
  • Political speeches

    • Sample Solution

    Question
    10
    (Recommendation Engines)

    Find a sample from the dataset that is closest to the listed candidate:

    1 dimensional

    The person closes in age to Alice who is 9 years old.
    Out[]=

    2 dimensional

    Which city is closer to Champaign?
    Out[]=

    3 Dimensional

    Which color is closest to
    ?
    Out[]=
    What are the three different types of techniques used for recommendation engines?
    Explain, with the help of an example, the difference between supervised learning and unsupervised learning.
    Explain, with the help of an example, the difference between the two common types of supervised machine learning: classification and regression.
    How can unsupervised learning like clustering be used when you need a classifier model to predict labels for new data, but do not have labeled training examples?
    What type of machine learning would you use in each of the following cases? Explain why?
    Your choices are:
    ◼
  • Supervised learning: Regression (How much or how many)
    • ◼
  • Supervised learning: Classification (Is this A or B)
    • ◼
  • Unsupervised learning: Clustering (How is the data organized? Are there groups and outliers?)
    • 1
    .
    Searching the App Store: search for an app and you see various similar and recommended apps
    2
    .
    Facebook:- You post a picture and FB can automatically tag people in the picture
    3
    .
    Gmail:- You are typing your email .... And gmail can automatically suggest the remainder of the sentence
    4
    .
    State Farm:- You are driving ... your smartphone in the car ... SF modulates your insurance rate based on your driving score
    5
    .
    Whole foods:- You go to a store ... pick items into your cart ... and just walk out without any checkout lines ... you are charged to your credit card.-
    6
    .
    Walmart:- Input: past data on what people have bought in the past ... Output: The organization of items in the aisles for optimal sales
    7
    .
    Amazon Alexa:- Input: Hearing a voice command while a TV is on and other people are talking in the background ... output: decode the voice command-
    8
    .
    SpaceX air-taxi:- Input: Data that shows where people get into taxis and where they get off ... output: Launch pad locations for drone-taxis
    Identify the input, hidden and output layers in the given neural network.
    List two reason why deep learning is so successful currently?
    List some applications of deep learning.
    Define the following:
    ◼
  • Authentication
    • ◼
  • Encryption
    • ◼
  • Cryptography
    • Explain in your own words how cryptography can be used as a tool for informatics, business, finance, politics, human rights—any sector that deals with personal information or requires communication.
    Explain how each of the following works and also list an advantage or disadvantage
    ◼
  • Secret key cryptography
    • ◼
  • Public key cryptography
    • List a few examples of digital technologies used in home security
    How can computing be used to implement physical security?
    List some problems in using something like Machine Learning in real life applications
    List some advantages vs. disadvantages of using machine learning over humans.
    In class we discussed the following:
    ​Ethics: moral principles that govern ... the conducting of an activity.
    ​Privacy: the state or condition of being free from being observed or disturbed by other people.
    Describe a hypothetical scenario where use of data and machine learning models trained on the data, are used unethically and compromises user’s privacy.
    Offer a solution to the problem.

    Sample Solution

    What is the idea of “net neutrality”?
    Provide examples where
    ◼
  • You will trade your privacy for internet service benefits
    • ◼
  • You will not trade your privacy for internet service benefits

    • Sample Solution

    Match the words with the definitions ...
    Pay attention to the information on the last two slides on most lecture presentations--those are good candidates for this set of questions.