Basics of Machine Learning

"Basics of Machine Learning" is designed to provide participants with a comprehensive understanding of the fundamental concepts and tools of machine learning. The course covers key topics such as probability density estimation, linear regression, classification techniques like linear discriminants, logistic regression, and support vector machines, as well as ensemble methods such as bagging and boosting. Additionally, the course introduces the basics of deep neural networks, laying the groundwork for more advanced learning techniques.

Throughout the course, students will gain a solid foundation in the fundamental approaches of machine learning. By working on practical exercises, participants will cement their understanding of the techniques covered and gain valuable hands-on experience.

By the end of the course, students will have the knowledge and skills required to confidently utilize machine learning tools and techniques in their own projects, providing a strong foundation for further study or professional development in this rapidly evolving field.

What you'll learn

• Definition of Statistical Machine Learning
• Probability density estimation
• Definition and behavior of linear discriminant models
• Linear regression
• Logistic regression
• Support Vector Machines
• Ensemble Methods
• Basics of Neural Networks