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Different types of learning model

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And the second branch of machine learning is supervised learning. In supervised learning, as the training data is unlabeled, which means you don’t need to provide any label to the computer. And the computer will try to learn without a teacher. If you put all of the training set into the computer, and then they try to learn and produce the information. That is unsupervised learning. And here are all of the unsupervised learning algorithem examples. Like clustering. In clustering, you have k-Means, Hierarchical Cluster Analysis, Expectation Maximizations. Or even you also have some very popular techniques, like the PCA, also like the LLE or t-SNE, here. And another branch of unsupervised learning is that you can have some association rule learning here.
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And if we combined between supervised learning and unsupervised learning, you can have another technique. We call semi-supervised learning. And for semi-supervised learning like some algorithms can deal with partially labeled training data, usually a lot of unlabeled data, and a little bit of label data. So that’s why we call the same supervised learning. So you combine supervised and unsupervised learning together. And here is a slide that I can give you as a summary of different types of learning. So here, in supervised learning, you have all data as label, and then get the model. If semi-supervised learning, so you provide some label later and some unlabeled data, and after that, you generate the model.
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And the last one for unsupervised learning, you have all unlabeled data and generate the model. And another branch of machine learning, we can call reinforcement learning. And this one is a very different base, and this is called an agent in this context, can observe the environment, select the perform actions. And get rewards in return. So means that when you train the reinforcement learning, they get the information from the environment. And then provide the results. Nowadays, reinforce learning will be used a lot in robotics, try to create some robot, and then because the robot can learn by itself. And the second one, because I already mentioned about machine learning.
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And natural language processing are the most popular techniques that we will apply in bioinformatics. Therefore I try to explain a little bit about natural language processing, after machine learning here. And natural language processing, NLP is a field of linguistics computer science, and also artificial intelligence concerned with the interactions between computers and human language. And in particular how to program computers to process and analyze large amounts of natural language data. And some challenges in natural language processing frequently involved like speech recognition, natural language understanding, and also natural language generation. And you can… if you hear about NLP, you see, some of the applications of NLP such as a very popular one is the google translate.
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Like you can… when they try to provide a natural language processing technique to try to understand the language, and then help you to automatically translate the language. And some of the branch of natural language processing is include like test classification information retrieval natural language generation, or even natural language understanding. And mostly in bioinformatics the one, we use a lot is text classification, which is the first branch of natural language processing. And for test classification, you try to use this to classify the sequence of proteins or DNA in bioinformatics.

Dr. Khanh will introduce unsupervised learning, supervised learning, and reinforcement learning in this video.

Unsupervised Learning is a machine learning technique in which the users do not need to supervise the model. Instead, it allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with the unlabelled data.

Supervised learning is defined by its use of labeled datasets to train algorithms that to classify data or predict outcomes accurately. As input data is fed into the model, it adjusts its weights through a reinforcement learning process, which ensures that the model has been fitted appropriately.

Next, he will explain Natural Language Processing(NLP). It is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of the human languages in a manner that is valuable. Most NLP techniques rely on machine learning to derive meaning from human languages.

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Artificial Intelligence in Bioinformatics

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