CS339 Artificial Intelligence
Writing Exercise in Style and Composition
The following exercise is borrowed from Style: The Basics of Clarity and Grace, 5th Ed. by Joseph
Williams and Joseph Bizup.
To write clear, direct, concise statements, we must follow basic principles of good
style.
Lesson 1: Put the most important parts of a sentence in the most important place.
Lesson 1a: Identify the main action of a sentence. Make this action the central verb.
Lesson 1b: Identify the main actor of a sentence. Make this actor the central subject.
Lesson 1c: Place lesser information in subjugating roles.
Example:
Poor: The cause of our schools' failure at teaching basic
skills is not understanding the influence of cultural
background on learning.
central actor = school
central action = fail
actual subject = the cause (which is a verb used as a noun = nomilization)
actual verb = is ( a weak verb the confers no information )
Better: Our schools have failed to teach basic skills because
they do not understand how cultural background influences
the way a child learns.
Notice the subjugating idea "cultural background influences the way a child learns"
is placed in a subordinate clause.
poor:
The identification of data centers and the visualization of
patterns of data which may contain more dimensions than can
be reasonably plotted is one of the most important aspects
of the k-means algorithm.
subject = a big mess at the front
verb = is
main actor = k-means
main action = identify (as noun), visualize
better:
The k-means algorithm identifies clusters and allows us to
visualize patterns in high dimensional datasets.
better:
The k-means algorithm identifies clusters and reveals
patterns in high dimensional datasets.
Try on your own
(i) Identify the subject
(ii) Identify the verb
(iii) Identify the main actor
(iv) Identify the main action
(v) Rewrite the sentence
The effectiveness of classification of the multi-layer neural network is compared for
three different data sets.
Each cluster location is chosen randomly generated, but each cluster starts on a set of
numerical values which represents a particular color.
Data Compression, on the other hand, is a technique in computer science that will
constantly try to improve the space used for saving data. For this reason, it is important
to optimize the amount of compression that takes place for images such that we can
save images with minor losses that do not affect the image.
Our first data set is The Iris Dataset. This dataset has been widely used and is extremely
well understood, which makes for a great test set for our k-means classifications.
In the American government, people often think of congress as being a highly partisan
environment. There are Democrats and Republicans, and there aren’t any other groups.
While it is true that there are only two parties represented (with sometimes a few
Independents), we won- der whether there really are two main voting blocks or if there
are more, how unified each group actually is, and how good a predictor party alignment
is for determining which group an individual will be placed into.
Unsupervised machine learning aims to locate clusters of data without being explicitly
told which data points are similar[2]. k- Means is an unsupervised machine learning
algorithm that divides a set of data into k categories[2].