# Linear Regression and Modeling

Linear Regression and Modeling
We will use linear regression models to explore the relationships between variables in a data set and a continuous outcome, linear model specification, and explanatory variables. We will also use regression diagnostics to determine underlying causes of regression.

You will need to have a basic knowledge of statistics and machine learning to be able to use the models and specifications in this course.Linear Regression Methods and Modeling
Linear Model Specification
Model Diagnostics and Model Run Quality
Exploring Univariate Regression
Machine Learning in python
This course is all about Machine Learning and its applications in the data science world. We will learn about the most important algorithms and techniques for constructing models for data science, and how to apply those techniques to solve problems in various specialties. We will also learn about deep learning techniques that can help with tasks like recognizing faces from videos. These techniques are applied in the context of a wide variety of scientific and engineering problems, and are all based on the linear model principle. We will also learn about different types of deep learning models and how to evaluate their performance.

This is a deep dive course, and will require some prerequisite knowledge of python. We will start with a short description of what machine learning is, the basic concepts, how it differs from regular neural networks, and how they perform. We will then explain the common techniques used for classification problems, including linear models, random forests, and multilayer perceptrons. We will also learn about the most popular deep learning techniques – SGD, RBFR, and convolutional neural networks.

If you are new to python programming, please review the tutorial “Getting Started” and “Working with numpy and numpy modules”.

We hope you enjoy this course, and look forward to seeing you in class!The Linear Regression in Python
Linear Models and ML
Deep Learning for Science
Deep Learning for Engineering
Monte Carlo simulation
This is a general purpose simulation tool based on the popular Arnold-Hinton game theory. It is suitable for general purpose simulation, computer vision, and many other fields. It can run on GPU-hardened hardware (Intel x86_64 or AMD64), as well as other modern operating systems. It is compatible with most common programming tools, and uses the popular OpenCL framework. The simulator uses a very simple language: Python, so you can learn python without learning any special knowledge. The program has a number of libraries, including the optimization toolchain, a number of simulation libraries (including ones for numpy, matplotlib, and flex), as well as a number of other small tools and a large number of lines of code. The minimal required software to run the program and build the simulator is a python-based distro like Ubuntu 14.04 LTS, or equivalent