Introduction to AI and ML

By the Cylance Data Science Team

Artificial intelligence (AI) encompasses a range of technologies intended to endow computers with human-like capabilities for learning, reasoning, and drawing user insights. This book introduces you to the machine learning (ML) techniques and methods applicable to modern security problems. 

Introduction to AI and ML

By the Cylance Data Science Team

Artificial intelligence (AI) encompasses a range of technologies intended to endow computers with human-like capabilities for learning, reasoning, and drawing user insights. This book introduces you to the machine learning (ML) techniques and methods applicable to modern security problems. 
Chapter 1: Clustering Chapter 1: Clustering

Chapter 1: Clustering

A variety of techniques for subdividing samples into distinct clusters with similar key features and attributes. Clustering is particularly useful in data exploration and forensic analysis – for sifting through vast quantities of data to identify outliers and anomalies requiring further investigation. 

 

 

Chapter 2: Classification Chapter 2: Classification

Chapter 2: Classification

Classification encompasses a set of computational methods for predicting the likelihood that a given sample belongs to a predefined class or type of file. For example, whether a given piece of email should be marked as spam.  
Chapter 3: Probability Chapter 3: Probability

Chapter 3: Probability

We consider probability as a predictive modeling technique for classifying and clustering samples. This includes foundational concepts such as trial, outcome, and event, along with the differences between joint and conditional types of probability. 
Chapter 4: Deep Learning Chapter 4: Deep Learning

Chapter 4: Deep Learning

Deep learning is primarily based on the use of neural networks, a class of algorithms named for how they simulate the way neurons interact in the brain. In this chapter we look at how two types of neural networks can be applied to effectively solve a classification problem. 

About Our Authors

Brian Wallace

Experienced in software engineering, reverse engineering, malware analysis, machine learning, vulnerability research, cryptography, and more.  

Sepehr Akhavan-Masouleh

Sepehr works on the application of statistical and machine learning models in cybersecurity. Ph.D.,  University of California. 

Andrew Davis

Andrew is a neural network wizard wielding a Ph.D. in Computer Engineering from University of Tennessee. 

Mike Wojnowicz

Mike enjoys developing and deploying large-scale probabilistic models due to their interpretability. Ph. D., Cornell University. 

John H. Brock

Researches applications of machine learning to static malware detection and analysis. M.S. Computer Science, University of California.  

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