Welcome to Deep Reinforcement Learning 2.0! In this course, we will learn and implement a new incredibly smart AI model, called the Twin-Delayed DDPG, which combines state of the art techniques in Artificial Intelligence including continuous Double Deep Q-Learning, Policy Gradient, and Actor Critic. The model is so strong that for the first time in our courses, we are able to solve the most challenging virtual AI applications (training an ant/spider and a half humanoid to walk and run across a field). In this part we will study all the fundamentals of Artificial Intelligence which will allow you to understand and master the AI of this course. These include Q-Learning, Deep Q-Learning, Policy Gradient, Actor-Critic and more.

This is the SECOND in a series of posts on applying Tim Ferriss' accelerated learning framework to Data Science. My goal is to become a world-class (top 5%) Data Scientist in 6 months, while open-sourcing everything I find and learn along the way. And if you stick around until the end, you're in for a special treat. A simple Google search of "how to learn Data Science" returns thousands of learning plans, degree programs, tutorials, and bootcamps. It's never been more difficult for a beginner to find signal in the noise. Everyone seems to have a different opinion, and the only common approach appears to be dumping a long list of courses to take and books to read, all the while providing little to no context into how these concepts fit into the bigger picture.

A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. One, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Two, a Bayesian network can be used to learn causal relationships, and hence can be used to gain understanding about a problem domain and to predict the consequences of intervention. Three, because the model has both a causal and probabilistic semantics, it is an ideal representation for combining prior knowledge (which often comes in causal form) and data. Four, Bayesian statistical methods in conjunction with Bayesian networks offer an efficient and principled approach for avoiding the overfitting of data. In this paper, we discuss methods for constructing Bayesian networks from prior knowledge and summarize Bayesian statistical methods for using data to improve these models. With regard to the latter task, we describe methods for learning both the parameters and structure of a Bayesian network, including techniques for learning with incomplete data. In addition, we relate Bayesian-network methods for learning to techniques for supervised and unsupervised learning. We illustrate the graphical-modeling approach using a real-world case study.

Digital transformation is affecting manufacturing and engineering industries across global markets. Institutions, companies and professionals are adopting Artificial Intelligence (AI) at a rapid rate to create efficiencies, new products and services and respond to market dynamics. AI technologies hold the promise of creating smarter, safer, efficient and more secure systems. AI is being developed on quantum computers and on multiple distributed edge nodes, while systems are becoming more responsive in thinking, perceiving and acting within time performance constraints. IISE's one-day symposium "AI: Impact on Industrial and Systems Engineering" is the perfect vehicle to start gathering the tools and knowledge needed to explore the impact Artificial Intelligence will have on industrial and systems engineers and the businesses/enterprises they manage.

How to create an API for machine learning. Let's dive into data science with python and learn how we can create our own API (Application Programming Interface) where we can send data to and let our model return a prediction. This course is a practical hands on course where we learn to deploy our trained machine learning models aka neural networks with the flask web framework. This is a beginners class. You don't need any pre-knowlege about flask but you should know about neural networks and python.

Learn to use functions and apply Codes. This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning and also the basics of Machine learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand and its application . Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path.

In this course you will learn all the machine learning models that has vast applications and mostly used. We will cover all the mathematics behind every Machine Learning Model so that you understand what actually happens behind the scene and how we actually train the machine to make future decision. We will then move on to implement all machine learning models in Python. After taking this course, you guys should be able to know not just the implementation part but also you will have a genuine understanding of every model behind the scenes.

In this course you will learn all the machine learning models that has vast applications and mostly used. We will cover all the mathematics behind every Machine Learning Model so that you understand what actually happens behind the scene and how we actually train the machine to make future decision. We will then move on to implement all machine learning models in Python. After taking this course, you guys should be able to know not just the implementation part but also you will have a genuine understanding of every model behind the scenes.

Natalie Lao was set on becoming an electrical engineer, like her parents, until she stumbled on course 6.S192 (Making Mobile Apps), taught by Professor Hal Abelson. Here was a blueprint for turning a smartphone into a tool for finding clean drinking water, or sorting pictures of faces, or doing just about anything. "I thought, I wish people knew building tech could be like this," she said on a recent afternoon, taking a break from writing her dissertation. After shifting her focus as an MIT undergraduate to computer science, Lao joined Abelson's lab, which was busy spreading its App Inventor platform and do-it-yourself philosophy to high school students around the world. App Inventor set Lao on her path to making it easy for anyone, from farmers to factory workers, to understand AI, and use it to improve their lives.

Ensemble methods have been widely used for improving the results of the best single classification model. Indeed, a large body of works have achieved better results mainly by applying one specific ensemble method. However, very few works analyze complex fusion schemes using heterogeneous ensemble strategies. This paper is three-fold: 1) It provides a tutorial of the most popular ensemble methods, 2) analyzes the best ensembles using MNIST as guiding thread and 3) shows that complex fusion architectures based on heterogeneous ensembles can be considered as a mode of taking benefit from diversity. We introduce a complex fusion design that achieves a new record in MNIST with only 10 misclassified images.