AI&U Episode 6 AI and Algorithms by AI&U - Sharad Gandhi and Christian Ehl published on 2018-08-16T12:07:16Z Algorithms, Machine Learning and Deep Learning Algorithm: Formal Definition: An algorithm is a procedure or formula for solving a problem, based on conducting a sequence of specified actions. A computer program can be viewed as an elaborate algorithm. Or simply, it is a set of steps to accomplish a task. Our view: Algorithm is a model (mathematically – a target function) that best correlates (predicts) output behavior to all possible input combinations. ==== Machine Learning Formal Definition: Machine learning is the process of teaching a computer to carry out a task, rather than programming it how to carry that task out step by step. At the end of training, a machine-learning system will be able to make accurate predictions for all possible input data. Our View: Machine Learning is a method of arriving at the right algorithm for a machine for most accurately correlating outputs with all combinations of input data. It uses labeled data inputs to check, tune, and improve its accuracy. ==== Deep Learning (a form of ML using neural networks) AI develops an algorithm on its own from the data inputs during the training phase. The machine literally learns. The machine-developed algorithm is a predictive algorithm and “labels” what the input data means. It is a snapshot and not a logical sequence of steps. Machine Learning using algorithms • For a general learning task where we would like to make predictions in the future (Y) given new examples of input variables (X). We don’t know what the function (f) looks like or its form. If we did, we would use it directly and we would not need to learn it from data using machine learning algorithms. • The most common type of machine learning is to learn the mapping Y = f(X) to make predictions of Y for new X. This is called predictive modeling or predictive analytics and our goal is to make the most accurate predictions possible. Artificial intelligence: Intelligence exhibited by machines for tasks that would typically require human intelligence. ==== Machine learning is typically split into: • Supervised learning, where the computer learns by example from labeled data • Unsupervised learning, where the computer groups similar data and pinpoints anomalies. Neural networks are mathematical models whose structure is loosely inspired by that of the brain. All neural networks have an input layer, where the initial data is fed in, and an output layer, that generates the final prediction. But in a deep neural network, there will be multiple "hidden layers" of neurons between these input and output layers, each feeding data into each other. Hence the term "deep" in "deep learning" and "deep neural networks", it is a reference to the large number of hidden layers -- typically greater than three -- at the heart of these neural networks.