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Introduction to Machine Learning Dr Shaimaa Elmorsy  Machine learning Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machi...

Introduction to Machine Learning Dr Shaimaa Elmorsy  Machine learning Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.  Machine learning In 1959, the term “machine learning” was first introduced by Arthur Samuel. He defined it as the “field of study that gives computers the ability to learn without being explicitly programmed”. The learning process improves the machine model over time by using training data. The evolved model is used to make future predictions.  Machine learning A machine learning model is a mathematical representation of a real-world process. A model in a computer is a mathematical function that represents a relationship or mapping between a set of inputs and a set of outputs. Example:  Sample Applications Web search Computational biology Finance E-commerce Space exploration Robotics Information extraction Social networks Debugging software [Your favorite area]  Machine learning approaches The machine learning algorithm is a technique through which the system extracts useful patterns from historical data. These patterns can be applied to new data. The objective is to have the system learn a specific input/output transformation. The data quality is critical to the accuracy of the machine learning results.  Machine learning approaches Types of learning 1. Supervised Learning 2. Unsupervised Learning 3. Semi-Supervised Learning 4. Reinforcement Learning  Supervised learning: Train by using labeled data, and learn and predict new labels for unseen input data. Classification is the task of predicting a discrete class label (categories), such as “black, white, or gray” and “tumor or not tumor”. Regression is the task of predicting a continuous quantity, such as “weight”, “probability”, and “cost”.  Unsupervised learning Detect patterns and relationships between data without using labeled data. Clustering algorithms: Discover how to split the data set into a number of groups such that the data points in the same groups are more similar to each other compared to data points in other groups.  Semi-supervised learning A machine learning technique that falls between supervised and unsupervised learning. It includes some labeled data with a large amount of unlabeled data. Here is an example that uses pseudo-labeling a. Use labeled data to train a model. b. Use the model to predict labels for the unlabeled data. c. Use the labeled data and the newly generated labeled data to create a new model.  Reinforcement learning Reinforcement learning uses trial and error (a rewarding approach). The algorithm discovers an association between the goal and the sequence of events that leads to a successful outcome. Example reinforcement learning applications: Robotics: A robot that must find its way. Self-driving cars.  Evaluation Accuracy Precision and recall Squared error Likelihood Posterior probability Cost / Utility Margin Entropy K-L divergence etc.  Machine learning algorithms Supervised learning -Decision tree induction -Neural networks & deep learning Unsupervised learning -Clustering (K-means clustering)