Machine Learning and its subsequent fields have undergone tremendous growth in the past few years. It has a number of potential applications and is being used in different fields...

1. Hot Machine learning topics
Machine Learning and its subsequent fields have undergone tremendous growth
in the past few years. It has a number of potential applications and is being used
in different fields. A lot of research work is going on in this field. There has been a
lot of buzz around this field in the recent times. It is the major application of
Artificial Intelligence. Algorithms are a major component of Machine Learning.
One should have a complete understanding of these algorithms before doing
research on different topics in Machine Learning. There are various topics in
Machine Learning for M.Tech thesis and Ph.D. research.
Here is the list of hot machine learning topics 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
Deep Learning is a sub-field of Machine Learning or we can say it is an advanced
version of Machine Learning. Deep Learning can also be referred to as deep
structure learning or hierarchical learning. It is one of the hot topics in machine
learning for master’s thesis and research. The concept of deep learning is being
used by big companies like Google, Amazon to increase their productivity and sale
rate.
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.

2. 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
Limitations of Deep Learning
There are some limitations of deep learning which are as follows:
 It needs a large amount of data to extract results.
 Substantial computational power and resources are required by deep
neural networks.

3.  Deep Learning is a time-consuming process.
 Training is to be provided so as to enable deep learning to make decisions.
 A high-performance computing environment is required for deep learning.