AI Software that Learns using Probabilistic and Statistical Models

Machine learning algorithms have an extensive history and are broadly categorised as supervised or unsupervised. These algorithms can generalise solutions to a wide range of problems. They are currently used extensively in modelling.

Supervised and unsupervised machine learning algorithms are the two primary subcategories. Using labelled training data, which comprises both the input data and the associated accurate output, supervised learning algorithms are trained. A supervised learning algorithm's objective is to develop a model that can correctly anticipate the result from fresh, unforeseen input data. Unsupervised learning algorithms, on the other hand, do not have access to labelled training data. Instead, they must find patterns and relationships in the data on their own. These algorithms are useful for tasks such as clustering and dimensionality reduction.

A wide range of mathematical techniques in order to learn from data and make predictions. These techniques include Linear Algebra, Calculus, Probability Theory, Information Theory and Statistics. Machine learning algorithms also make use of other mathematical ideas and methods in addition to the ones just discussed in order to learn from data and produce predictions. Probability, being the study of random occurrences and their likelihood of occurring, is a key idea in machine learning.

Machine learning makes predictions based on partial or ambiguous data using the principles of probability theory to describe uncertainty. Optimisation is also used which entails minimising or maximising some objective function to identify the best solution to a problem. In machine learning, optimisation techniques are frequently employed to identify the model parameter values that would minimise the difference between the expected and actual results.

Advantages of Machine Learning

• Ability to learn from data: Without explicit programming, machine learning algorithms are able to learn from data and enhance their performance over time.
• Machine learning algorithms have shown to be particularly effective at modelling complex and non-linear interactions, handling complex and non-linear connections well.
• Processing vast amounts of data efficiently is a strength of machine learning algorithms, which makes them suitable for jobs like pattern identification and predictive modelling.
• Generalisation skills: Machine learning algorithms can apply their expertise to fresh, unexplored data, which is why they are effective for predicting and classification jobs.

Limitations of Machine Learning

• Dependence on high-quality data: A machine learning model's performance is strongly correlated with the high quality of the training data. The model could not work well if the data are skewed or noisy.
• Results can be challenging to interpret, and it can be challenging to comprehend how a machine learning model comes to its conclusions.
• Machine learning models are susceptible to overfitting, which can cause them to perform well on training data but poorly on fresh, untried data.
• Sensitivity to hyperparameter selection: Depending on the choice of specific hyperparameters, such as the learning rate and the number of hidden layers, a machine learning model's performance may be affected. Due to this, optimising the model's performance may be difficult.

Machine learning has been widely adopted and used for a variety of applications across a wide range of sectors. These consist of natural language processing, pattern recognition, and predictive modelling. They are widely used in modelling, and they are especially good at resolving challenging non-linear problems.

Telemus AI™ can help you and your organisation understand better how Artificial Intelligence can solve your most complex problems and challenges.

Contact us today to see how Telemus AI™ can be used in your organisation.