Hot Topics in Machine Learning For Research and Thesis
Hot Topics in Machine Learning For Research and Thesis
Hot Topics in Machine Learning For Research and Thesis
Here is the list of hot topics in Machine Learning for thesis and research:
Deep Learning
Human-computer interaction
Genetic Algorithm
Image Annotation
Reinforcement Learning
Natural Language Processing
Supervised Learning
Unsupervised Learning
Support Vector Machines(SVMs)
Sentiment Analysis
Deep Learning
The algorithms in deep learning or deep neural networks are concerned with the functioning of
the human brain and its structure. Deep Neural Network is a type of neural network having more
than two layers. This type of neural network needs more data as well as the computational power
to derive results.
Applications of Deep Learning
Deep Learning applications will significantly affect our daily life in near future. Some of the
applications have already made their impact. Here are some of the important applications of deep
learning:
Image Recognition
Voice Assistants
Self-driving cars
Computer-aided medical diagnosis
Automatic Machine Translation
Human-computer Interaction
Human-computer interaction or HCI is the study of human and computer activities and how they
interact with each other. It is a very good field for research in machine learning. There are
different ways in which humans interact with computers and HCI deals with the study of this
interaction. To facilitate this interaction, an interface is required between humans and computers.
A graphical user interface is one such example of the interface used by desktop applications and
internet browsers. Similarly, voice user interfaces(VUI) are used for speech recognition.
The idea of HCI dates back to early 1980s. It is a very broad field covering the areas like user-
centered design, user experience design, and user interface design. Research work is going on the
following areas of HCI:
Augmented Reality
Social Computing
Brain-computer interface
User Customization
Embedded Computation
Genetic Algorithm
The concept of Genetic Algorithm is based on the principle of Genetics and Natural Selection
and is a search-based optimization technique used to find optimal solutions to complex problems.
It is another good topic in machine learning for thesis and research. It is the most efficient tool to
solve difficult problems referred to as NP-Hard problems.
Genetic Algorithms are important in machine learning and are based on the following three types
of rules:
Automotive Design
Robotics
Encryption
Computer-Aided Design
Bioinformatics
Machine Learning Feature Selection
Mutation Testing
Software Engineering
Image Annotation
There are various tools for manual image annotation some of which are listed below:
DataTurks
Labelbox
AnnoStation
LabelMe
Pixorize
Microsoft VoTT
Images Annotation Programme
FastAnnotationTool
Reinforcement Learning
Reinforcement Learning is a type of machine learning algorithm in which an agent learns how to
behave in an environment by interacting with that environment. A lot of research has been done
in this area of machine learning in the recent times. It mostly finds its application in gaming and
robotics. The approach of this algorithm is different from other machine learning algorithms
which are supervised learning and unsupervised learning.
The definition of reinforcement learning can be understood with the following concepts:
The importance of natural language processing lies in the fact that it enables computers to
communicate with humans in their own language. Computers can interpret human speech and
text using the concept of natural language processing. It will help to analyze the large volumes of
textual data generated every day.
Applications of Natural Language Processing
Speech Recognition
Language Translation
Caption Generation
Language Modelling
Optical Character Recognition(OCR)
Information Retrieval
Question Answering
Sentiment Analysis
Text Segmentation
Document Clustering
Supervised Learning
It is a type of machine learning algorithm in which both input and output data is provided and the
output data is mapped to the input through a mapping function. In other words, supervised
learning is a type of machine learning algorithm that uses training datasets for making decisions.
There are two types of algorithms in supervised learning which are:
Classification - Where data can be categorized into specific classes for categorical
response values. Commonly used classification algorithms are:
o Support Vector Machines(SVM)
o Neural Networks
o Decision Trees
o Discriminant Analysis
o Nearest neighbors(kNN)
Regression - Regression algorithms are used for continuous-response values. Following
algorithms are included in this category:
o Linear Regression
o Nonlinear Regression
o Generalized linear models
o Decision Trees
o Neural Networks
Supervised learning has a number of applications including algorithm trading, credit scoring,
tumor detection, drug discovery, pattern recognition, price forecasting to name a few. It is also a
very good thesis topic in machine learning.
Unsupervised Learning
Unsupervised Learning is a type of machine learning algorithm to find hidden patterns and
underlying data structures. The inferences are drawn by this algorithm from the datasets
containing the input data. Cluster Analysis is the most commonly used method in unsupervised
learning. General clustering algorithms in unsupervised learning are:
Hierarchical Clustering
k-Means Clustering
Self-organizing maps
Hidden Markov Models
Gaussian mixture models
The main purpose of unsupervised learning is to group data having similar characteristics into
different clusters. This done by finding datasets having similar patterns. Apart from clustering,
Principal Component Analysis(PCA) is another commonly used technique in unsupervised
learning. Unsupervised Learning finds its application in data mining, text mining, bioinformatics,
image segmentation, computer vision, and genetic clustering.
Support Vector Machines or SVMs are one of the most important machine learning algorithms.
The purpose of this algorithm is to analyze the data used for classification and regression
analysis. As compared to other algorithms, the concepts of SVM are relatively simple. Using
kernel trick, SVMs can perform non-linear classification. In this algorithm, each data item is
plotted as a point in n-dimensional space where n is the number of features. After that,
classification is performed on data items. A hyperplane will be found that will divide the datasets
into two different classes.
Image Classification
Face Detection
Bioinformatics
Text Categorization
Handwriting Recognition
Protein Remote Homology Detection
Sentiment Analysis
Sentiment Analysis is also known as opinion mining and is a process to determine whether the
attitude of an individual towards a product or topic is positive, negative 0r neutral expressed in
the form of text. It is another good topic in machine learning for thesis and research. It uses the
concept of natural language processing, machine learning, computational linguistics, and
bioinformatics to extract essential information. It is mainly used in case of social media
monitoring. Sentiment Analysis is crucial such that it helps to find what a customer thinks of a
particular brand.
Sentiment Analysis Tools
Google Analytics
Hootsuite
Pagelever
Marketing Grader
Facebook Insights
Google Alerts
Meltwater
Tweetstats
Sentiment Analysis can be used in different areas for different purposes. Following are some of
the applications of sentiment analysis:
Online Commerce
Voice of the Customer(VOC)
Voice of the Market(VOM)
Brand Reputation Management
Voting advise applications
Government Intelligence
These are the hot topics in Machine Learning for thesis and research although there are various
other topics also. Machine Learning is one of the trending fields for the thesis in computer
science.