Practice-Oriented Introduction to Machine Learning : Linear Regression, Decision Tree, and Single Layer Perceptron models : Учебно-методическое пособие.

Normatov S., Nesterov P.V., Aliev T.A., Timralieva A.A., Novikov A.S., Skorb E.V. Practice-Oriented Introduction to Machine Learning : Linear Regression, Decision Tree, and Single Layer Perceptron models : Учебно-методическое пособие. - Санкт-Петербург: Университет ИТМО, 2024. - 51 с. - экз.

Аннотация :

       This manual aims to provide some basic knowledge on Machine Learning algorithms for the Infochemistry Scientific Center students. It is a practice-oriented tutorial which serves as guideline for the beginners in their desire to build their own Machine Learning models. The Database (DB) used in this work contains the energy values of chemical systems, obtained via Quantum Chemical calculations. However, the protocol mentioned here not only applicable for calculated data, but also for any type of tabular values (including experimental data). The manual covers the main aspects of Machine Learning process, which include exploratory data analysis (EDA), data preprocessing, training, validation, and visualization. The authors provide some examples on three Machine Learning algorithms, namely: Linear Regression, Decision Tree, and Single Layer Perceptron. These three algorithms are considered to be good starting  points (in that they are the least complex) in their respective classes: linear, tree-based, and neural networks.

       The manual can be useful for teaching Bachelor students of the “Infochemistry” educational program at ITMO University and is within the modules of the following courses: “Infochemistry”, “Artificial Intelligence and Machine Learning in Chemistry”.

Описание :

       Рекомендовано к использованию в Университете ИТМО по направлению подготовки 04.03.01, 18.03.01 в качестве Учебно-методическое пособие для реализации основных профессиональных образовательных программ высшего образования бакалавриата.

Название : PRACTICE-ORIENTED INTRODUCTION Скачать PDF (3478.05 Кб)