Information Technology: Instructional Materials



What Is Probability?

#artificialintelligence

Uncertainty involves making decisions with incomplete information, and this is the way we generally operate in the world. Handling uncertainty is typically described using everyday words like chance, luck, and risk. Probability is a field of mathematics that gives us the language and tools to quantify the uncertainty of events and reason in a principled manner. In this post, you will discover a gentle introduction to probability. Photo by Emma Jane Hogbin Westby, some rights reserved.


Free Book: Lecture Notes on Machine Learning

#artificialintelligence

Lecture notes for the Statistical Machine Learning course taught at the Department of Information Technology, University of Uppsala (Sweden.) Available as a PDF, here (original) or here (mirror). B.1 A general iterative solution B.2 Commonly used search directions


On Education Natural Language Processing with Deep Learning in Python - all courses

#artificialintelligence

Understand and implement word2vec Understand the CBOW method in word2vec Understand the skip-gram method in word2vec Understand the negative sampling optimization in word2vec Understand and implement GloVe using gradient descent and alternating least squares Use recurrent neural networks for parts-of-speech tagging Use recurrent neural networks for named entity recognition Understand and implement recursive neural networks for sentiment analysis Understand and implement recursive neural tensor networks for sentiment analysis Install Numpy, Matplotlib, Sci-Kit Learn, Theano, and TensorFlow (should be extremely easy by now) Understand backpropagation and gradient descent, be able to derive and code the equations on your own Code a recurrent neural network from basic primitives in Theano (or Tensorflow), especially the scan function Code a feedforward neural network in Theano (or Tensorflow) Helpful to have experience with tree algorithms In this course we are going to look at advanced NLP. Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices. These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words. In this course I'm going to show you how to do even more awesome things. We'll learn not just 1, but 4 new architectures in this course.


On Education Natural Language Processing with Deep Learning in Python - all courses

#artificialintelligence

Understand and implement word2vec Understand the CBOW method in word2vec Understand the skip-gram method in word2vec Understand the negative sampling optimization in word2vec Understand and implement GloVe using gradient descent and alternating least squares Use recurrent neural networks for parts-of-speech tagging Use recurrent neural networks for named entity recognition Understand and implement recursive neural networks for sentiment analysis Understand and implement recursive neural tensor networks for sentiment analysis Install Numpy, Matplotlib, Sci-Kit Learn, Theano, and TensorFlow (should be extremely easy by now) Understand backpropagation and gradient descent, be able to derive and code the equations on your own Code a recurrent neural network from basic primitives in Theano (or Tensorflow), especially the scan function Code a feedforward neural network in Theano (or Tensorflow) Helpful to have experience with tree algorithms In this course we are going to look at advanced NLP. Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices. These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words. In this course I'm going to show you how to do even more awesome things. We'll learn not just 1, but 4 new architectures in this course.


On Education Data Science: Deep Learning in Python - all courses

#artificialintelligence

This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE. We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.


The Ease of Wolfram Alpha, the Power of Mathematica: Introducing Wolfram

#artificialintelligence

Wolfram Alpha has been a huge hit with students. Whether in college or high school, Wolfram Alpha has become a ubiquitous way for students to get answers. But it's a one-shot process: a student enters the question they want to ask (say in math) and Wolfram Alpha gives them the (usually richly contextualized) answer. It's incredibly useful--especially when coupled with its step-by-step solution capabilities. But what if one doesn't want just a one-shot answer?


Investment Management with Python and Machine Learning Coursera

#artificialintelligence

The practice of investment management has been transformed in recent years by computational methods. This course provides an introduction to the underlying science, with the aim of giving you a thorough understanding of that scientific basis. However, instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language. This course is the first in a four course specialization in Data Science and Machine Learning in Asset Management but can be taken independently. In this course, we cover the basics of Investment Science, and we'll build practical implementations of each of the concepts along the way.


How artificial intelligence will help shape the future of higher education?

#artificialintelligence

Artificial Intelligence (AI) as an idea seems to have caught the imagination of both industry and academia alike. Although AI-related academic research has been in place since the late nineties but it is recently that products and services inspired by AI have emerged out of labs into our daily routine activities. Whether it is the buzz around autonomic vehicles, drones, speech recognition, various voice response systems like Alexa and Google assistant, every single one of these products has some form of AI at its core. Undoubtedly, ever-increasing processing speeds and storage capacities along with possibilities of machine to machine (M2M) communication have let the cat out of the bag. Today we produce more data in a single day then possibly we did in the entire year in the eighties.


Georgia Center offers course on machine learning and data science for executives - UGA Public Service and Outreach

#artificialintelligence

The UGA Center for Continuing Education & Hotel now offers a new course for business executives that demystifies machine learning and data science and focuses on the practical applications of these revolutionary technologies in business. Taught by Jagannath Rao, a professor in the UGA College of Engineering and a senior vice president of data services for Siemens, the course is designed for people with little to no coding experience, and provides hands-on experience building and implementing data science projects. Participants in the inaugural class were from Lockheed Martin, Quicklogic Corporation, Sandia National Laboratories, Recro Gainesville Development LLC, and NASA, among others. "This course focused on making AI practical in a corporate environment and is geared to leaders tasked with setting such strategy for their business," said Chris Rogers from SensiML Corp, who was in the first class.