courseraПрограммирование

Introduction to Learning

Курс от University of Colorado Boulder

Начальный≈ 5.8 чАнглийский

О курсе

This course introduces the foundational concepts of learning, focusing on supervised, unsupervised, and reinforcement learning. Students will learn how machines can learn from data to make predictions, find patterns, and make decisions over time. Topics include key algorithms such as decision trees, linear classifiers, clustering, and Q-learning. Students will develop a practical understanding of how learning systems work and how to apply them to real-world problems.

Навыки, которые вы освоите

Unsupervised Learning Supervised Learning Model Evaluation Reinforcement Learning Machine Learning Decision Tree Learning Model Training Artificial Intelligence and Machine Learning (AI/ML)Artificial Neural Networks Machine Learning Algorithms Data-Driven Decision-Making Machine Learning Methods Algorithms Applied Machine Learning Artificial Intelligence

Программа курса

4 модулей · 24 учебных материалов

01Foundations of Learning in AI 4 материалов

Learning Fundamentals

What is Learning? Видео Learning in Intelligent AgentsВидео Types of Learning - Supervised, Unsupervised, and ReinforcementВидео Learning Basics QuizЗадание

02Foundations of Machine Learning - Prediction and Structure7 материалов

Учитесь у экспертов

Rhonda Hoenigman

Преподаватель курса

Introduction to Learning

О курсе

Навыки, которые вы освоите

Программа курса

Learning Fundamentals

Учитесь у экспертов

Rhonda Hoenigman

Learning to Predict from Data

Learning Models and Discovering Structure

Assessment

Generalization and Model Behavior

Evaluating Model Performance

Classical Learning Models

From Models to Modern AI Systems

Culminating Quiz