Machine Learning

  MACHINE LEARNING Syllabus: UNIT-1 Introduction: Brief Introduction to Machine Learning, Abstraction and Knowledge Representation, Types of Machine Learning Algorithms, Definition of learning systems, Goals and applications of machine learning, Aspects of developing a learning system, Data Types, training data, concept representation, function approximation. Data Pre-processing: Definition, Steps involved in pre-processing, Techniques UNIT-2 Performance measurement of models: Accuracy, Confusion matrix, TPR, FPR, FNR, TNR, Precision, recall, F1-score, Receiver Operating Characteristic Curve (ROC) curve and AUC. Supervised Learning1: Linear Regression, Multiple Variable Linear Regression, Naïve Bayes Classifiers, Gradient Descent, Multicollinearity, Bias-Variance trade-off. UNIT-3 Supervised Learning2 : Regularization, Logistic Regression, Squashing function, KNN, Support Vector Machine. Decision Tree Learning: Representing concepts as decision trees, Recursive induction of decisi

  Types of Machine Learning Systems There are so many different types of Machine Learning systems that it is useful to classify them in broad categories, based on the following criteria: •         Whether or not they are trained with human supervision a.       supervised, b.      unsupervised, c.       semi supervised, and d.      Reinforcement Learning •         Whether or not they can learn incrementally on the fly a.       online learning b.      batch learning •         Whether they work by simply comparing new data points to known data points , or instead by detecting patterns in the training data and building a predictive model , much like scientists do a.       instance-based b.      model-based learning These criteria are not exclusive; you can combine them in any way you like. ·        For example, a state-of-the-art spam filter may learn on the fly using a deep neural network model trained using examples of spam and ham; ·        this makes i

  4. Introduction to Supervised, Unsupervised and Reinforcement Learning   ü   The amount of data generated in the world today is very huge. This data is generated not only by humans but also by smartphones, computers and other devices. Based on the kind of data available and a motive present, certainly, a programmer will choose how to train an algorithm using a specific learning model. ·        Machine Learning  is a part of Computer Science where the efficiency of a system improves itself by repeatedly performing the tasks by using data instead of explicitly programmed by programmers. Further let us understand the difference between three techniques of Machine Learning- Supervised, Unsupervised and Reinforcement Learning.   How Machine Learning Works? Types of Learning Algorithms        i.           Supervised learning      ii.           Unsupervised learning    iii.           Reinforcement learning Supervised Learning ·        In Supervised learning, an AI