There are various assumptions for this function. Everything is dependent on machine learning. Find out what are the benefits of machine learning. Decision making is faster — Machine learning provides the best possible outcomes by prioritizing the routine decision-making processes.
Adaptability — Machine Learning provides the ability to adapt to new changing environment rapidly. The environment changes rapidly due to the fact that data is being constantly updated. Innovation — Machine learning uses advanced algorithms that improve the overall decision-making capacity.
This helps in developing innovative business services and models. Insight — Machine learning helps in understanding unique data patterns and based on which specific actions can be taken. Business growth — With machine learning overall business process and workflow will be faster and hence this would contribute to the overall business growth and acceleration. Outcome will be good — With machine learning the quality of the outcome will be improved with lesser chances of error.
Computational Learning Theory — Computational learning theory is a subfield of machine learning for studying and analyzing the algorithms of machine learning. It is more or less similar to supervised learning. Adversarial Machine Learning — Adversarial machine learning deals with the interaction of machine learning and computer security.
The main aim of this technique is to look for safer methods in machine learning to prevent any form of spam and malware. It works on the following three principles:. Finding vulnerabilities in machine learning algorithms. Devising strategies to check these potential vulnerabilities. Implementing these preventive measures to improve the security of the algorithms. Quantum Machine Learning — This area of machine learning deals with quantum physics. In this algorithm, the classical data set is translated into quantum computer for quantum information processing.
Predictive Analysis — Predictive Analysis uses statistical techniques from data modeling, machine learning and data mining to analyze current and historical data to predict the future. It extracts information from the given data. Customer relationship management CRM is the common application of predictive analysis.
Robot Learning — This area deals with the interaction of machine learning and robotics. It employs certain techniques to make robots to adapt to the surrounding environment through learning algorithms. Grammar Induction — It is a process in machine learning to learn formal grammar from a given set of observations to identify characteristics of the observed model. Grammar induction can be done through genetic algorithms and greedy algorithms.
Meta-Learning — In this process learning algorithms are applied on meta-data and mainly deals with automatic learning algorithms. Here is a list of artificial intelligence and machine learning tools for developers:. Protege — It is a free and open-source framework and editor to build intelligent systems with the concept of ontology.
It enables developers to create, upload and share applications. It has a collection of tools which can be used by developers and in business. DiffBlue — It is another tool in artificial intelligence whose main objective is to locate bugs, errors and fix weaknesses in the code.
All such things are done through automation. TensorFlow — It is an open-source software library for machine learning. TensorFlow provides a library of numerical computations along with documentation, tutorials and other resources for support. Amazon Web Services — Amazon has launched toolkits for developers along with applications which range from image interpretation to facial recognition. It implements neural networks.
It has a lot of tutorials and documentation along with an advanced tool known as Neural Designer. Apache Spark — It is a framework for large-scale processing of data. It also provides a programming tool for deep learning on various machines. Caffe — It is a framework for deep learning and is used in various industrial applications in the area of speech, vision and expression.
Following are some of the applications of machine learning:. Machine Learning in Bioinformatics. Bioinformatics term is a combination of two terms bio, informatics. Bio means related to biology and informatics means information. Thus bioinformatics is a field that deals with processing and understanding of biological data using computational and statistical approach. Machine Learning has a number of applications in the area of bioinformatics.
Machine Learning find its application in the following subfields of bioinformatics:. Genomics — Genomics is the study of DNA of organisms. Machine Learning systems can help in finding the location of protein-encoding genes in a DNA structure. Gene prediction is performed by using two types of searches named as extrinsic and intrinsic. Machine Learning is used in problems related to DNA alignment.
Proteomics — Proteomics is the study of proteins and amino acids. Proteomics is applied to problems related to proteins like protein side-chain prediction, protein modeling, and protein map prediction. Microarrays — Microarrays are used to collect data about large biological materials. Machine learning can help in the data analysis, pattern prediction and genetic induction. It can also help in finding different types of cancer in genes.
System Biology — It deals with the interaction of biological components in the system. Machine Learning help in modeling these interactions. Text mining — Machine learning help in extraction of knowledge through natural language processing techniques.
Deep Learning is a part of the broader field machine learning and is based on data representation learning. It is based on the interpretation of artificial neural network. Deep Learning algorithm uses many layers of processing. Each layer uses the output of previous layer as an input to itself. The algorithm used can be supervised algorithm or unsupervised algorithm. Deep Learning is mainly developed to handle complex mappings of input and output.
It is another hot topic for M. Tech thesis and project along with machine learning. Deep Neural Network is a type of Artificial Neural Network with multiple layers which are hidden between the input layer and the output layer. This concept is known as feature hierarchy and it tends to increase the complexity and abstraction of data. This gives network the ability to handle very large, high-dimensional data sets having millions of parameters. The procedure of deep neural networks is as follows:.
Consider some examples from a sample dataset. Improve weight of the network to reduce the error. Here are some of the applications of Deep Learning:. Deep Learning helps in solving certain complex problems with high speed which were earlier left unsolved. Deep Learning is very useful in real world applications.
Following are some of the main advantages of deep learning:. Eliminates unnecessary costs — Deep Learning helps to eliminate unnecessary costs by detecting defects and errors in the system. Identifies defects which otherwise are difficult to detect — Deep Learning helps in identifying defects which left untraceable in the system.
Can inspect irregular shapes and patterns — Deep Learning can inspect irregular shapes and patterns which is difficult for machine learning to detect. From this introduction, you must have known that why this topic is called as hot for your M.
Tech thesis and projects. This was just the basic introduction to machine learning and deep learning. There is more to explore in these fields. You will get to know more once you start doing research on this topic for your M. Tech thesis. You can get thesis assistance and guidance on this topic from experts specialized in this field.
Here is the list of current research and thesis topics in Machine Learning :. Machine Learning Algorithms. Supervised Machine Learning. What further points can be made? If there are "always alternatives" to the problem the student is identifying, then why bother developing a paper around that claim? Ideally, a thesis statement should be complex enough to explore over the length of the entire paper.
In the revised thesis, you can see the student make a specific, debatable claim that has the potential to generate several pages' worth of discussion. When drafting a thesis statement, think about the questions your thesis statement will generate: What follow-up inquiries might a reader have?
In the first example, there are almost no additional questions implied, but the revised example allows for a good deal more exploration. If you are having trouble getting started, try using the models below to generate a rough model of a thesis statement! These models are intended for drafting purposes only and should not appear in your final work. When drafting your thesis statement, avoid words like explore, investigate, learn, compile, summarize , and explain to describe the main purpose of your paper.
These words imply a paper that summarizes or "reports," rather than synthesizing and analyzing. Instead of the terms above, try words like argue, critique, question , and interrogate. These more analytical words may help you begin strongly, by articulating a specific, critical, scholarly position. Read Kayla's blog post for tips on taking a stand in a well-crafted thesis statement. Didn't find what you need? Search our website or email us. Read our website accessibility and accommodation statement.
Writing a Paper: Thesis Statements. Print Page Report a broken link. Basics of Thesis Statements The thesis statement is the brief articulation of your paper's central argument and purpose. Being Specific This thesis statement has no specific argument: Needs Improvement: In this essay, I will examine two scholarly articles to find similarities and differences.
Better: In this essay, I will argue that Bowler's autocratic management style, when coupled with Smith's theory of social cognition, can reduce the expenses associated with employee turnover. Making a Unique Argument This thesis draft repeats the language of the writing prompt without making a unique argument: Needs Improvement: The purpose of this essay is to monitor, assess, and evaluate an educational program for its strengths and weaknesses.
Then, I will provide suggestions for improvement. Better: Through a series of student interviews, I found that Kennedy High School's antibullying program was ineffective. In order to address issues of conflict between students, I argue that Kennedy High School should embrace policies outlined by the California Department of Education Creating a Debate This thesis statement includes only obvious fact or plot summary instead of argument: Needs Improvement: Leadership is an important quality in nurse educators.
Better: Roderick's theory of participatory leadership is particularly appropriate to nurse educators working within the emergency medicine field, where students benefit most from collegial and kinesthetic learning. Choosing the Right Words This thesis statement uses large or scholarly-sounding words that have no real substance: Needs Improvement: Scholars should work to seize metacognitive outcomes by harnessing discipline-based networks to empower collaborative infrastructures.
Better: Ecologists should work to educate the U. Leaving Room for Discussion This thesis statement is not capable of development or advancement in the paper: Needs Improvement: There are always alternatives to illegal drug use. Better: The most effective treatment plan for methamphetamine addiction may be a combination of pharmacological and cognitive therapy, as argued by Baker , Smith , and Xavier Extra Tips Thesis Mad Libs If you are having trouble getting started, try using the models below to generate a rough model of a thesis statement!
Words to Avoid and to Embrace When drafting your thesis statement, avoid words like explore, investigate, learn, compile, summarize , and explain to describe the main purpose of your paper. Related Resources.