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MACHINE LEARNING EXAM ALL QUESTIONS AND ANSWERS
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MACHINE LEARNING EXAM ALL QUESTIONSAND ANSWERS
What is Machine Learning?
It is the science and art of programming computers so they can learn from data.
We can using Machine Learning in data mining to
dig into large amounts of data which can help us discover patterns that are
immediately apparent.
What is Machine Learning great for?
Problems for which existing solutions require a lot of hand-tuning or long list of
rules. (ML can often simplify code and perform better)
Complex problems for which there is no good solution at all use traditional
approach.
Fluctuating environments. ( A ML system cab adapt to new data)
Getting insights about complex problems and large amounts of data.
Supervised Learning
The training data you feed to the algorithm includes the desired solutions called
labels
Unsupervised learning
The training data is unlabeled. The system tries to learn without a teacher.
One important unsupervised learning task is anomaly detection. ( We will detect
unusual credit transactions, manufacturing defects, and etc.)
Semi supervised learning
,Some algorithms can deal with partially labeled training data. It is usually a lot
of unlabeled data and a little bit of labeled data.
Reinforcement learning
is an area of machine learning concerned with how software agents ought to
take actions in an environment so as to maximize some notion of cumulative
reward. Reinforcement learning is one of three basic machine learning
paradigms, alongside supervised learning and unsupervised learning.
Batch Learning
Another criterion to classify Machine Learning is to tell whether or not the
system can learn incrementally from a stream of incoming data.
In batch learning the system is incapable of learning incrementally. It must be
trained using all the available data. This will generally take a lot of time and
computing resources so it is typically done offline. First the system is trained
and then it is launched into production and runs without learning anymore. It
just applies what it learned. That is called offline learning.
If you want batch learning system to know about new data (such as a new type
of spam), you need to train a new version of the system from scratch on the full
dataset. (Not t just the new data, but also the old data) Then you have to stop the
old system and replace it with the new one).
Online Learning
You train the system incrementally by feeding it data instances, sequentially,
either individually or by small groups called mini batches. Each step is fast and
cheap, so the system can learn about new data on the fly, as it arrives. (Online
learning is good for systems that receive continuous data on a flow. (Stock
prices)
One is one important parameter of Online Learning -
how fast they should adapt to changing data> This is called the learning rate. or
step size in machine learning is a hyperparameter which determines to what
extent newly acquired information overrides old information.[1] The learning
rate is often denoted by the character η or α.
,If you see a high learning rate, then your system will rapidly adapt to new data,
but it will also tend to quickly forget the old data. (you don't want a spam filter
to flag only the latest kinds of spam it was shown). If you set a low learning
rate, the system will have more inertia, that is. It will learn more slowly. But it
will also be less sensitive to noise in the new data or to sequences of non-
representative data points.
A bug challenge: is that if bad data is fed to the system, the system's
performance will gradually decline. If we are talking about a live system, your
clients will notice. For instance, bad data could come from a malfunctioning
sensor on a robot or someone spamming a search engine to try and rank high in
search results. To reduce this risk. You need to monitor the system closely and
promptly switch learning off ( and revert to the previous state) if you detect a
drop in performance. May also want to monitor the input data and react to
abnormal data (using an anomaly detection algorithm)
Anomaly detection algorithm
is a technique used to identify unusual patterns that do not conform to expected
behavior, called outliers.
instance-based learning
It is a family of learning algorithms that, instead of performing explicit
generalization, compares new problem instances with instances seen in training,
which have been stored in memory.
It just about storing the training data instances. Though the training data itself
can be preprocessed in many ways and stored in memory.
is a family of learning algorithms that, instead of performing explicit
generalization, compares new problem instances with instances seen in training,
which have been stored in memory.
It is called instance-based because it constructs hypotheses directly from the
training instances themselves.
This means that the hypothesis complexity can grow with the data:
in the worst case, a hypothesis is a list of n training items and the computational
complexity of classifying a single new instance is O(n). One advantage that
, instance-based learning has over other methods of machine learning is its ability
to adapt its model to previously unseen data. Instance-based learners may
simply store a new instance or throw an old instance away.
Examples of instance-based learning algorithm are the k-nearest neighbors
algorithm, kernel machines and RBF networks.
These store (a subset of) their training set; when predicting a value/class for a
new instance, they compute distances or similarities between this instance and
the training instances to make a decision.
Called that the system learns the examples by heart, then generalizes to new
cases using similarity measure.
Categorizing Machine Learning Algorithms:
How they generalize. Most Machine Learning tasks are about making
predictions. This means that given a number of training examples, the system
needs to be able to generalize to examples, it has never seen before. Having a
good performance measure on the training data is good, insufficient; the true
goal is to perform well on new instances:
Generalization: Instance-Based learning and model-based learning.
Model based learning
Another way to generalize from a set of examples is to build a model of these
examples, then use that model to make predictions.
Challenges in a Machine Learning Project
Insufficient Quantity of Training Data
- it takes a lot of data for most machine learning algorithms to work properly.
Even for such simple problems you typically need thousands of examples, and
for complex problems such as image or speech recognition you may need
millions of examples (unless you can reuse parts of an existing model).
-Non-representative training data -
In order to generalize well, it is crucial that your training data be representative
of the new cases you want to generalize. This is true whether you use instance-
based learning or model based learning
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