Learn how to build state of the art models without needing graduate-level math—but also without dumbing anything down.This 7-week course is designed for anyone with at least
Learn the most important machine learning models, including how to create them yourself from scratch, as well as key skills in data preparation, model validation, and building
This is an introductory course by Caltech Professor Yaser Abu-Mostafa on machine learning that covers the basic theory, algorithms, and applications. Machine learning (ML) ena
Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a t
Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of
Introduction and mathematical preliminaries:What is Pattern recognition; Applications and Examples - Clustering vs. Classification; Supervised vs. unsupervised - Relevant basi
Contents:
Overview of Pattern classification and regression : Introduction to Statistical Pattern Recognition - Overview of Pattern Classifiers
Bayesian decision making and
Basic ideas and techniques underlying the design of intelligent computer systems. Topics include heuristic search, problem solving, game playing, knowledge representation, log
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