Markov Models Supervised And Unsupervised Machine Learning Pdf
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Hidden Markov models HMMs form a class of statistical models in which the system being modeled is assumed to be a Markov process with hidden states. From observed output sequences generated by the Markov process, both the output emission probabilities from the hidden states and the transition probabilities between the hidden states can be estimated by using dynamic programming methods.
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Machine Learning tutorial provides basic and advanced concepts of machine learning.
The reader must have basic knowledge of artificial intelligence. The Software Engineering View. With the abundance of datasets available, the demand for machine learning is in rise. Essentials of Machine Learning Algorithms with Python and R Codes Find, read and cite all the research you need on ResearchGate If you are new to this arena, we suggest you pick up tutorials based on these concepts first, before you embark on with Machine Learning.
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Most of human and animal learning is unsupervised learning. If intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake. We need to solve the unsupervised learning problem before we can even think of getting to true AI. In this chapter, we will explore the difference between a rules-based system and machine learning, the difference between supervised learning and unsupervised learning, and the relative strengths and weaknesses of each.
We will also cover many popular supervised learning algorithms and unsupervised learning algorithms and briefly examine how semisupervised learning and reinforcement learning fit into the mix. These input variables are also known as features or predictors or independent variables. The set of examples the AI trains on is known as the training set , and each individual example is called a training instance or sample. During the training, the AI is attempting to minimize its cost function or error rate , or framed more positively, to maximize its value function —in this case, the ratio of correctly classified emails.
The AI actively optimizes for a minimal error rate during training. However, what we care about most is how well the AI generalizes its training to never-before-seen emails. This will be the true test for the AI: can it correctly classify emails that it has never seen before using what it has learned by training on the examples in the training set? This generalization error or out-of-sample error is the main thing we use to evaluate machine learning solutions.
This set of never-before-seen examples is known as the test set or holdout set because the data is held out from the training. If we choose to have multiple holdout sets perhaps to gauge our generalization error as we train, which is advisable , we may have intermediate holdout sets that we use to evaluate our progress before the final test set; these intermediate holdout sets are called validation sets.
To put all of this together, the AI trains on the training data experience to improve its error rate performance in flagging spam task , and the ultimate success criterion is how well its experience generalizes to new, never-before-seen data generalization error. But this system would be difficult to maintain over time as bad guys change their spam behavior to evade the rules. If we used a rules-based system, we would have to frequently adjust the rules manually just to stay up-to-date.
Also, it would be very expensive to set up—think of all the rules we would need to create to make this a well-functioning system. Instead of a rules-based approach, we can use machine learning to train on the email data and automatically engineer rules to correctly flag malicious email as spam. This machine learning-based system could be automatically adjusted over time as well. This system would be much cheaper to train and maintain. In this simple email problem, it may be possible for us to handcraft rules, but, for many problems, handcrafting rules is not feasible at all.
For example, consider designing a self-driving car—imagine drafting rules for how the car should behave in each and every single instance it ever encounters. This is an intractable problem unless the car can learn and adapt on its own based on its experience. We could also use machine learning systems as an exploration or data discovery tool to gain deeper insight into the problem we are trying to solve. For example, in the email spam filter example, we can learn which words or phrases are most predictive of spam and recognize newly emerging malicious spam patterns.
The field of machine learning has two major branches— supervised learning and unsupervised learning —and plenty of sub-branches that bridge the two. In supervised learning, the AI agent has access to labels, which it can use to improve its performance on some task. In the email spam filter problem, we have a dataset of emails with all the text within each and every email. We also know which of these emails are spam or not the so-called labels. These labels are very valuable in helping the supervised learning AI separate the spam emails from the rest.
In unsupervised learning, labels are not available. Therefore, the task of the AI agent is not well-defined, and performance cannot be so clearly measured. Consider the email spam filter problem—this time without labels. Now, the AI agent will attempt to understand the underlying structure of emails, separating the database of emails into different groups such that emails within a group are similar to each other but different from emails in other groups.
This unsupervised learning problem is less clearly defined than the supervised learning problem and harder for the AI agent to solve. But, if handled well, the solution is more powerful. In other words, because the problem does not have a strictly defined task, the AI agent may find interesting patterns above and beyond what we initially were looking for. Moreover, this unsupervised system is better than the supervised system at finding new patterns in future data, making the unsupervised solution more nimble on a go-forward basis.
This is the power of unsupervised learning. Supervised learning excels at optimizing performance in well-defined tasks with plenty of labels. For example, consider a very large dataset of images of objects, where each image is labeled. If the dataset is sufficiently large enough and we train using the right machine learning algorithms i.
As the supervised learning AI trains on the data, it will be able to measure its performance via a cost function by comparing its predicted image label with the true image label that we have on file.
The AI will explicitly try to minimize this cost function such that its error on never-before-seen images from a holdout set is as low as possible. This is why labels are so powerful—they help guide the AI agent by providing it with an error measure. The AI uses the error measure to improve its performance over time. However, the costs of manually labeling an image dataset are high. And, even the best curated image datasets have only thousands of labels.
This is a problem because supervised learning systems will be very good at classifying images of objects for which it has labels but poor at classifying images of objects for which it has no labels. As powerful as supervised learning systems are, they are also limited at generalizing knowledge beyond the labeled items they have trained on.
In other words, supervised learning is great at solving narrow AI problems but not so good at solving more ambitious, less clearly defined problems of the strong AI type.
Supervised learning will trounce unsupervised learning at narrowly defined tasks for which we have well-defined patterns that do not change much over time and sufficiently large, readily available labeled datasets. However, for problems where patterns are unknown or constantly changing or for which we do not have sufficiently large labeled datasets, unsupervised learning truly shines.
Instead of being guided by labels, unsupervised learning works by learning the underlying structure of the data it has trained on. It does this by trying to represent the data it trains on with a set of parameters that is significantly smaller than the number of examples available in the dataset.
By performing this representation learning, unsupervised learning is able to identify distinct patterns in the dataset.
In the image dataset example this time without labels , the unsupervised learning AI may be able to identify and group images based on how similar they are to each other and how different they are from the rest. For example, all the images that look like chairs will be grouped together, all the images that look like dogs will be grouped together, etc.
Instead of labeling millions of images by hand, humans can manually label all the distinct groups, and the labels will apply to all the members within each group. After the initial training, if the unsupervised learning AI finds images that do not belong to any of the labeled groups, the AI will create separate groups for the unclassified images, triggering a human to label the new, yet-to-be-labeled groups of images. Unsupervised learning makes previously intractable problems more solvable and is much more nimble at finding hidden patterns both in the historical data that is available for training and in future data.
Moreover, we now have an AI approach for the huge troves of unlabeled data that exist in the world. Even though unsupervised learning is less adept than supervised learning at solving specific, narrowly defined problems, it is better at tackling more open-ended problems of the strong AI type and at generalizing this knowledge. Just as importantly, unsupervised learning can address many of the common problems data scientists encounter when building machine learning solutions.
Recent successes in machine learning have been driven by the availability of lots of data, advances in computer hardware and cloud-based resources, and breakthroughs in machine learning algorithms. But these successes have been in mostly narrow AI problems such as image classification, computer vision, speech recognition, natural language processing, and machine translation. To solve more ambitious AI problems, we need to unlock the value of unsupervised learning.
I think AI is akin to building a rocket ship. You need a huge engine and a lot of fuel. To build a rocket you need a huge engine and a lot of fuel. If machine learning were a rocket ship, data would be the fuel—without lots and lots of data, the rocket ship cannot fly. But not all data is created equal.
To use supervised algorithms, we need lots of labeled data, which is hard and costly to generate. With unsupervised learning, we can automatically label unlabeled examples. Here is how it would work: we would cluster all the examples and then apply the labels from labeled examples to the unlabeled ones within the same cluster.
Unlabeled examples would receive the label of the labeled ones they are most similar to. If the machine learning algorithm learns an overly complex function based on the training data, it may perform very poorly on never-before-seen instances from holdout sets such as the validation set or test set. In this case, the algorithm has overfit the training data—by extracting too much from the noise in the data—and has very poor generalization error.
In other words, the algorithm is memorizing the training data rather than learning how to generalize knowledge based off of it. To address this, we can introduce unsupervised learning as a regularizer. Regularization is a process used to reduce the complexity of a machine learning algorithm, helping it capture the signal in the data without adjusting too much to the noise. Unsupervised pretraining is one such form of regularization.
Instead of feeding the original input data directly into a supervised learning algorithm, we can feed a new representation of the original input data that we generate. This new representation captures the essence of the original data—the true underlying structure—while losing some of the less representative noise along the way.
When we feed this new representation into the supervised learning algorithm, it has less noise to wade through and captures more of the signal, improving its generalization error. Even with the advances in computational power, big data is hard for machine learning algorithms to manage. In general, adding more instances is not too problematic because we can parallelize operations using modern map-reduce solutions such as Spark.
However, the more features we have, the more difficult training becomes. In a very high-dimensional space, supervised algorithms need to learn how to separate points and build a function approximation to make good decisions. When the features are very numerous, this search becomes very expensive, both from a time and compute perspective. In some cases, it may be impossible to find a good solution fast enough.
This problem is known as the curse of dimensionality , and unsupervised learning is well suited to help manage this. With dimensionality reduction, we can find the most salient features in the original feature set, reduce the number of dimensions to a more manageable number while losing very little important information in the process, and then apply supervised algorithms to more efficiently perform the search for a good function approximation.
Feature engineering is one of the most vital tasks data scientists perform. Without the right features, the machine learning algorithm will not be able to separate points in space well enough to make good decisions on never-before-seen examples. However, feature engineering is typically very labor-intensive; it requires humans to creatively hand-engineer the right types of features.
Instead, we can use representation learning from unsupervised learning algorithms to automatically learn the right types of feature representations to help solve the task at hand. The quality of data is also very important.
Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science And Python
We give a tutorial and overview of the field of unsupervised learning from the perspective of statistical modeling. Unsupervised learning can be motivated from information theoretic and Bayesian principles. We briefly review basic models in unsupervised learning, including factor analysis, PCA, mixtures of Gaussians, ICA, hidden Markov models, state-space models, and many variants and extensions. We derive the EM algorithm and give an overview of fundamental concepts in graphical models, and inference algorithms on graphs. The aim of this chapter is to provide a high-level view of the field.
In this paper, we model operator states using hidden Markov models applied to human models obtained with two different supervised learning techniques and an methodology has been favored by the machine-learning community in the.
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This course offered as two successive modules to MSc students provides an in-depth introduction to statistical modelling, unsupervised, and some supervised learning techniques. It presents probabilistic approaches to modelling and their relation to coding theory and Bayesian statistics. A variety of latent variable models will be covered including mixture models used for clustering , dimensionality reduction methods, time series models such as hidden Markov models which are used in speech recognition and bioinformatics, Gaussian process models, independent components analysis, hierarchical models, and nonlinear models. The course will present the foundations of probabilistic graphical models e.
Most of human and animal learning is unsupervised learning. If intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake. We need to solve the unsupervised learning problem before we can even think of getting to true AI. In this chapter, we will explore the difference between a rules-based system and machine learning, the difference between supervised learning and unsupervised learning, and the relative strengths and weaknesses of each. We will also cover many popular supervised learning algorithms and unsupervised learning algorithms and briefly examine how semisupervised learning and reinforcement learning fit into the mix.
The Strengths and Weaknesses of Unsupervised Learning
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