AI skills: Introduction to Unsupervised, Deep and Reinforcement Learning

Topic 1: Introduction This is an introduction to the course with an overview of the topics. Topic 2: Clustering Clustering techniques are used to identify similar data/objects and patterns from your engineering datasets. You will learn about the problem of clustering, the main classes of clustering techniques and how we can implement k-means and hierarchical clustering. Introduction to clustering Formalizing the problem of clustering Similarity and dissimilarity measures k-means clustering Hierarchical clustering Topic 3: Dimensionality Reduction Dimensionality reduction techniques are used to reduce the number of features representing a given dataset, while retaining the structure of the dataset as much as possible. You will learn what dimensionality reduction is, why it is needed and how to use it. You will learn about Principal Component Analysis dimensionality reduction technique and how and when to apply it. What is dimensionality reduction? Why dimensionality reduction techniques are used Feature selection vs. feature extraction techniques Principal component analysis feature-extraction technique Topic 4: Introduction to Deep Learning Deep learning is a broader family of machine learning methods based on artificial neural networks. You will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about fully connected neural networks, some theoretical aspects of deep learning, the back-propagation algorithm, Adam, and much more. What neural networks are Architecture of fully connected neural networks Capabilities of neural networks The benefits of deep architectures Training deep neural networks with stochastic gradient descent and Adam Topic 5: Introduction to Reinforcement Learning Reinforcement learning teaches an AI to interact with an environment. You will be introduced to basic reinforcement learning concepts and techniques, and how they could be applied in real world applications. How an AI interacts with the environmen The formalization of this as a Markov Decision Process An agents behavior as a policy The optimal policy found using tabular Q-learning Why tabular reinforcement learning does not scale well Neural networks for deep reinforcement learning to scale up to interesting applications