A Technical Introduction to Machine Learning
Speaker: Justin Le.
Host: Prof. Yingtao Jiang.
Friday, Oct. 7, 2016
3 to 5pm
SEB 3265, UNLV
In recent years, intelligent algorithms that learn from data have had an enormous impact on empirical research in such diverse areas as medicine, physics, finance, and beyond. Furthermore, these algorithms have been implemented in common programming languages, making them widely accessible to both researchers and developers.
In this workshop, we'll discuss the concepts and mathematics that underlie these machine learning techniques, as well as the Python libraries that enable us to efficiently apply them in practice. Requiring only a basic familiarity with calculus and programming, the workshop will introduce common challenges in applying and evaluating machine learning methods with the goal of extracting insights from large, complex datasets.
Building flexible models and managing their complexity
Performance metrics and model evaluation
Features and representation
Applications to real-world data
Additional topics (as time permits)
Although not necessary, it is strongly recommended that you review the following topics before attending in order to fully benefit from the workshop:
If you wish to follow along with our programming exercises during the workshop, please bring your own laptop with Ubuntu 14.04+ and the following packages:
sudo apt-get install build-essential python-dev python-numpy \ python-numpy-dev python-scipy libatlas-dev g++ python-matplotlib \ ipython ipython-notebook pip install --upgrade pip pip install jupyter pip install -U scikit-learn
Pip is required for the above commands.
If you use your own distro instead, we cannot offer any support if you encounter issues while executing our code.
Find the repo here.