Introduction to Machine Learning | UCBerkeley

Learn a wide range of machine learning algorithms, including perceptrons, SVMs, and neural networks, from renowned expert Prof. Jonathan Shewchuk.

Introduction

This class introduces algorithms for learning, which constitute an important part of artificial intelligence. Topics include classification, regression, density estimation, dimensionality reduction, and clustering.

Highlights

• Covers a wide range of machine learning algorithms, including perceptrons, support vector machines, Gaussian discriminant analysis, logistic regression, decision trees, neural networks, and more.
• Taught by Professor Jonathan Shewchuk, a renowned expert in the field.
• Provides a solid foundation in the mathematical and theoretical concepts underlying machine learning.
• Includes hands-on programming assignments and projects to reinforce the concepts learned in class.

Recommendation

This course is recommended for students who have a strong background in mathematics (vector calculus, linear algebra, probability) and programming experience. It is an excellent choice for those interested in pursuing a career in artificial intelligence, machine learning, or data science.