Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include:
(i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
(ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
(iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Linear Regression with One Variable
prediction, present the notion of a cost function, and introduce the gradient descent method for learning.
Linear Algebra Review
course, especially as we begin to cover models with multiple variables.
Linear Regression with Multiple Variables
features. We also discuss best practices for implementing linear regression.
regularization, which helps prevent models from overfitting the training data.”’
Neural Networks: Representation
Neural Networks: Learning
module, you will be implementing your own neural network for digit recognition.
Advice for Applying Machine Learning
practice, and discuss the best ways to evaluate performance of the learned models.
Machine Learning System Design
discuss how to understand the performance of a machine learning system with multiple parts, and also how to deal with skewed data.
Support Vector Machines
discuss how to use it in practice.
enable us to learn groupings of unlabeled data points.
algorithms as well as for visualizations of complex datasets.
Large Scale Machine Learning
learning algorithms with large datasets.
Application Example: Photo OCR
problem and how to analyze and improve the performance of such a system.