Machine learning and artificial intelligence

Foundations of machine learning

The course typically begins with an introduction to basic concepts in machine learning, including supervised and unsupervised learning and reinforcement learning. Students learn about the algorithms and techniques used for pattern recognition and prediction.
Feature analysis and engineering:
Understanding and preparing data is an essential aspect of machine learning. Students learn data cleaning and analysis techniques, as well as feature engineering to enhance the performance of machine learning models.

Supervisory learning algorithms:

The course covers a range of supervised learning algorithms, such as linear regression, qualitative support mechanisms, decision trees, and neural networks. Students gain details about when to use different algorithms based on the nature of the problem.

Unsupervised learning algorithms:
Unsupervised learning focuses on discovering patterns and relationships in data without explicit labels. The course covers clustering and dimensionality reduction techniques, such as k-means clustering and principal components analysis.

Neural networks and deep learning:
Deep learning, a branch of machine learning, involves neural networks with multiple layers. Students delve deeper into engineering, training, and optimizing it, exploring applications such as image and speech recognition.

Reinforcement learning:
Reinforcement learning involves training agents to make sequential decisions by interacting with the environment. Students learn about decision-making processes in depth, such as Markov decision processes, Q-learning, and gradient policy methods.

Natural Language Processing (NLP):
NLP focuses on enabling machines to understand, interpret and generate human language. Students explore techniques for sentiment analysis, text summarization and language translation.

Evaluate and publish models:
Evaluating the performance of machine learning models is vital. The course covers metrics for evaluating models and strategies for deploying them in real-world applications.

Ethics and Responsible AI:
As AI applications become more widespread, ethical considerations come to the fore. Students can examine the impact of artificial intelligence on society and learn about artificial intelligence practices