Machine Learning 2023spring in UCSB
An introduction to the field of Machine Learning (ML), which attempts to understand and build machine learning algorithms. Topics include classification, clustering, representation, ensemble methods, neural networks and deep learning.
Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data," in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task (Wikipedia). We will cover the following topics:
Machine Learning Basic Concept
Classification
Clustering
Ensemble Methods
Neural Networks, Deep Learning
By the end of the course, you will understand what ML is all about and what it has contributed, and may contribute, to computing. You will have a working knowledge of the basic tools of machine learning, which are applicable to a wide range of computing problems. You will be able to solve problems using classification and clustering. You will have experience building models that, to some degree, can learn from experience.
This is not primarily a programming course - that is, the main goal is to learn the concepts, not to learn a language or particular programming techniques. However, coding examples of the concepts is the best way to demonstrate (and facilitate) your knowledge of them. Good programming practices (proper file structure, comments, etc.) are expected.