March 8-10, 2017
Montreal, Canada
Machine Learning Algorithms
This talk will show the most frequent used algorithms for machine learning, comparing the advantages and disadvantages for each of them.
Both supervised and unsupervised learning will be covered in this talk. We will walk through Linear Regression, Logistic Regression, Regularization and Support Vector Machines for Supervised Learning. For Unsupervised Learning, we will talk about Anomaly detection and Recommender Systems.
Both supervised and unsupervised learning will be covered in this talk. We will walk through Linear Regression, Logistic Regression, Regularization and Support Vector Machines for Supervised Learning. For Unsupervised Learning, we will talk about Anomaly detection and Recommender Systems.
Hanneli Tavante
Codeminer 42
Hanneli (@hannelita) is a developer addicted to code, learn new programming languages, blow capacitors, do some C programming and commit useful (or not) code for random Open Source Projects that she finds at Github. She tries to help the community by writing blog posts and organising meetups about NoSQL, programming languages and Math/Physics/Science. She also likes Math, Lego, dogs, hardware, and [much] coffee.
Montreal 2017 sponsored by
