Machine Learning Flashcards
Supervised Learning
A training set of examples with the correct responses (targets) is provided and, based on this training set, the algorithm generalises to respond correctly to all possible inputs.
This is also called learning from exemplars. Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.
In supervised learning, each example in the training set is a pair consisting of an input object (typically a vector) and an output value. A supervised learning algorithm analyzes the training data and produces a function, which can be used for mapping new examples. In the optimal case, the function will correctly determine the class labels for unseen instances.
Both classification and regression problems are supervised learning problems. A wide range of supervised learning algorithms are available, each with its strengths and weaknesses. There is no single learning algorithm that works best on all supervised learning problems.
Unsupervised Learning
Correct responses are not provided, but instead the algorithm tries to identify similarities between the inputs so that inputs that have something in common are categorised together. The statistical approach to unsupervised learning is known as density estimation.
Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. In unsupervised learning algorithms, a classification or categorization is not included in the observations. There are no output values and so there is no estimation of functions.
Since the examples given to the learner are unlabeled, the accuracy of the structure that is output by the algorithm cannot be evaluated. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data.
Reinforcement Learning
Reinforcement learning is somewhere between supervised and unsupervised learning. The algorithm gets told when the answer is wrong, but does not get told how to correct it. It has to explore and try out different possibilities until it works out how to get the answer right.
Reinforcement learning is sometime called learning with a critic because of this monitor that scores the answer, but does not suggest
improvements.
Reinforcement learning is the problem of getting an agent to act in the world so as to maximize its rewards. A learner (the program) is not told what actions to take as in most forms of machine
learning, but instead must discover which actions yield the most reward by trying them.
In the most interesting and challenging cases, actions may affect not only the immediate reward but also the next
situations and, through that, all subsequent rewards.
A classifier trained on less training data is less likely to overfit.
False:
A specific classifier (with some fixed model complexity)
will be more likely to overfit to noise in the training data when there is less training data,
and is therefore more likely to overfit.
True or False:
Given m data points, the training error converges to the true
error as m → ∞
True:
If we assume that the data points are Independent and Identically Distributed (i.i.d).
True or False:
The maximum likelihood model parameters (α) can be learned
using linear regression for the model: yi = α1x1x32
+ ǫi where ǫi ∼ N(0, σ2) i.i.d noise.
True:
y is linear in α1, so it can be learned using linear regres-
sion.
True or False:
The maximum likelihood model parameters (α) can be learned
using linear regression for the model: yi = xα1
1 eα2 + ǫi where ǫi ∼ N(0, σ2) i.i.d noise.
False
y is not linear in α1 and α2, and no simple transformation
will make it linear (log[xα1
1 eα2 + ǫi] 6= α1 log x1 + α2 + ǫi).
True or False:
The maximum likelihood model parameters (α) can be learned
using linear regression for the model: yi = log(xα1
1 eα2) + ǫi where ǫi ∼ N(0, σ2) i.i.d
noise.
True:
yi = log(xα1
1 eα2 ) + ǫi = α1 log x1 + α2 + ǫi, which is
linear in α1 and α2. Also, assuming x1 > 0.
Decision Tree
Decision Trees are a type of Supervised Machine Learning (that is you explain what the input is and what the corresponding output is in the training data) where the data is continuously split according to a certain parameter.
The tree can be explained by two entities, namely decision
nodes and leaves. The leaves are the decisions or the final outcomes. And the decision nodes are where the data is split.
Regression Tree
A decision tree that predicts a continuous variable.
