Kids will learn about object detection and tracking. We will start by installing OpenCV, a very popular library for computer vision. We will discuss frame differencing to see how we can detect the moving parts in a video. We will learn how to track objects using color spaces. We will understand how to use background subtraction to track objects. We will build an interactive object tracker using the CAMShift algorithm. We will learn how to build an optical flow-based tracker. We will discuss face detection and associated concepts such as Haar cascades and integral images. We will then use this technique to build an eye detector and tracker.
They will know about artificial neural networks. We will start with an introduction to artificial neural networks and the installation of the relevant library. We will discuss perceptions and how to build a classifier based on them. We will learn about single-layer neural networks and multilayer neural networks. We will see how to use neural networks to build a vector quantizer. We will analyze sequential data using recurrent neural networks. We will then use artificial neural networks to build an optical character recognition engine.
Kids will learn about reinforcement learning. We will discuss the premise of reinforcement learning. We will talk about the differences between reinforcement learning and supervised learning. We will go through some real-world examples of reinforcement learning and see how it manifests itself in various forms. We will learn about the building blocks of reinforcement learning and the various concepts involved. We will then create an environment in python to see how it works in practice. We will then use these concepts to build a learning agent.
They will learn about Deep Learning and Convolutional Neural Networks (CNNs). CNN’s have gained a lot of momentum over the last few years, especially in the field of image recognition. We will talk about the architecture of CNNs and the type of layers used inside. We are going to see how to use a package called TensorFlow. We will build a perceptron based linear regressor. We are going to learn how to build an image classifier using a single layer neural network. We will then build an image classifier using a CNN.