Introduction to Machine Learning | UC Berkeley CS 189 Course

Comprehensive machine learning course covering theoretical foundations, algorithms, and practical applications. Suitable for students with math and computer science background.

Introduction

Introductory ML course covering a wide range of topics: ranging from least squares to convolutional neural networks.

Highlights

• Covers theoretical foundations, algorithms, methodologies, and applications for machine learning
• Topics include supervised methods for regression and classification, generative and discriminative probabilistic models, and deep learning models
• Provides access to lecture slides and recordings for faster learning

Recommendation

This course is recommended for students interested in gaining a strong foundation in machine learning, covering both theoretical and practical aspects of the field. It is suitable for those with a background in mathematics and computer science.