Current Hot Topics in Machine Learning

 

Machine learning has changed over time. Machine learning is defined as recognizing various patterns and that computers can learn from the data provided or programmed into them. Though numerous machine learning models are utilized in earlier years, now, the innovative advancements facilitate to execute the complex mathematical calculations to the big data quickly in an automatic manner. 

With the evolution of machine learning, it has become important for researchers to see the adaptability level of computers and how fast they can perform tasks with the given data. Without even noticing, machine learning is used by everyone in daily life, for example, Google Assistant, Google Maps, Alexa, and many more. Machine learning is applied in the following areas:

 

• The importance of machine learning can be seen in self-driven Google cars.
• Recommendations provided by Amazon, Flipkart, Netflix, etc. online can be seen in our everyday life.
• One of the common machine learning applications is image and speech recognition.
• Another area where machine learning’s application is increasing day by day is predicting traffic. For example, Google maps show which route is the shortest one and without any traffic or less traffic. 

In this blog, we have covered current topics in machine learning from which you can take ideas for your thesis. 

Sum-Product Networks

Earlier, to understand and identify the dependencies and in-dependencies of several variables probabilistic graphical systems used a graph-based structure. The Sum-Product Network (SPN) is a relatively new kind of graphical system in which graph-based structure systems computations instead of connections between variables. The sum-product networks have attained magnificent results on countless data sets such as:

• Collaborative filtering
• Nucleic acid sequences
• Activity recognition
• Image Classification
• Image Completion
• Click-through logs

Large-Scale Matrix Factorization

The main purpose of matrix factorization is to set a huge data matrix and divide it into two or smaller matrices. They are commonly used in the reduction of dimensions and systems that offers recommendations. Probabilistic matrix factorization techniques can also be used to amplify the uncertainty in terms of quantity when providing recommendations. However, probabilistic matric factorization done on a large scale is often challenging in computational terms. Researchers can use the following thesis topic examples to prepare their thesis on machine learning:

 

• Developing scalable techniques for large-scale factorization.
• Developing probabilistic methods for implicit feedback.

Bayesian Deep Learning

Native deep neural networks do not predict uncertainty and neither they quantify the level of uncertainty. Whereas, Bayesian methods give an ideal way to predict and control the uncertainties. When we merge these two approaches i.e., deep neural networks and Bayesian methods, we get Bayesian neural networks. The main challenge lies in understanding and learning the meaning of Bayesian neural networks along with how to use them.

Machine Ethics

Unlike computer ethics, which refers to identifying issues related to the use of computers by humans, i.e., humans exploiting the computers or data stored in them; machine ethics is the opposite i.e., it identifies and rectifies the issues occurring in the functionality of the computers and its behavior towards the humans. The main idea behind machine ethics is to build a computer that itself follows ethical principles to be accountable for its actions and resolve the issues that it might encounter. Machine ethics allows the machines to make ethical decisions.   

Reinforcement Learning

Reinforcement learning is in which machine learning systems get trained to understand and learn how to make a series of decisions. Usually, to reach the goal, the agent goes through an uncertain and extremely complex environment. Whereas, in reinforcement learning, the machines go through a situation similar to video games. In this hit-and-trial method, the computers or machines learn to come up with an effective solution to the issues.

Supervised Learning

To prepare your thesis, you will need the help or training from your supervisor. Similarly, computers require special datasets or inputs to come up with the desired results. Thus, the name supervised learning. Like every machine learning system, supervised learning also uses training to perform the required tasks. Supervised learning is best suited for –

 

• Classification Systems: these systems recognize the problems with the output variables like ‘Yes’, ‘No, ‘Pass’, or ‘Fail’.
• Regression Systems: these systems recognize the problems with the output variable that has a real value like salary, dollars, weight, or a unique number.

Following are the practical applications of supervised learning in our daily life:

• Face detection
• Spam detection
• Signature recognition
• Text categorization
• Forecasting and predicting weather
• Predicting house prices as per the current market prices.
• Predicting stock market prices, and more.

Selecting a perfect topic for your thesis is not that easy. You have to be sure about your interests and the current trends going on in the field of machine learning. However, we have covered some of the current and hot topics in machine learning that are surely going to help you prepare a thesis on a good topic.