Linear Regression. var notice = document.getElementById("cptch_time_limit_notice_74"); About Slides • By popular demand, lecture slides will be made available online • They will show up just before a lecture starts • Slides are grouped by topic, not by lecture • Slides are not for studying • Class notes and homework assignments are the materials of record COMPSCI 371D — Machine Learning Introduction to Machine Learning 3 / 18 Week 1 (8/25 only): Slides for Machine Learning: An Overview (ppt, pdf (2 per page), pdf (6 per page)) Week 2 (8/30, 9 Mehryar Mohri - Introduction to Machine Learning page Examples of Learning Tasks Optical character recognition. The course is followed by two other courses, one focusing on Probabilistic Graphical Models and another on Deep Learning. Increasingly, extracting value from data is an important contributor to the global economy across a range of industries. Hey and welcome to my course on Applied Machine Learning. Go now belongs to computers. instances are typically examined independently. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project. .hide-if-no-js { Now customize the name of a clipboard to store your clips. We will study basic concepts such as trading goodness of fit and model complexity. If you continue browsing the site, you agree to the use of cookies on this website. Slides and notes may only be available for a subset of lectures. In this post, you got information about some good machine learning slides/presentations (ppt) covering different topics such as an introduction to machine learning, neural networks, supervised learning, deep learning etc. Lecture Slides and Lecture Videos for Machine Learning . It will also be of interest to engineers in the field who are concerned with the application of machine learning methods. Top 10 Types of Analytics Projects – Examples, Different Success / Evaluation Metrics for AI / ML Products, Andrew NG Machine Learning Coursera Videos, Linear Regression Explained with Real Life Example, Predictive vs Prescriptive Analytics Difference, Analytics Maturity Model for Assessing Analytics Practice, Different types of learning (supervised, unsupervised, reinforcement), Dimensions of a learning system (different types of feedback, representation, use of knowledge), Supervised learning algorithms such as Decision tree, neural network, support vector machines (SVM), Bayesian network learning, nearest neighbor models, Difference between supervised and unsupervised learning, Different machine learning algorithms for supervised learning, Decision tree (information gain theory, entropy, handling overfitting, and other issues), Model evaluation methods (hold-out, n-fold cross-validation, Leave-one-out cross-validation, validation set), Classification measures (precision, recall, F1 score, ROC curve, Sensitivity, Specificity, AUC, Scoring and ranking technique, ranking and lift analysis), Introduction to machine learning / deep learning with examples, Examples of features for machine learning, Introduction to neural networks, deep learning. Lecture 11: Introduction to Machine Learning Course Home Syllabus Readings Lecture Videos Lecture Slides and Files Assignments Software Download Course Materials Flash and JavaScript are required for this feature. Introduction Introduction The goal is prediction. Slides and notes may only be available for a subset of lectures. eight Machine Learning Predictor. If you are looking out for topics to be included in the machine learning course for your internal training purpose in your organization, the details presented below might turn out to be very helpful. }, Introduction to Machine Learning Lior Rokach Department of Information Systems Engineering Ben-Gurion University of the Negev. With a team of extremely dedicated and quality lecturers, machine learning introduction slides will not only be a place to share knowledge but also to help students get inspired to explore and discover many creative ideas from themselves. 1. See our Privacy Policy and User Agreement for details. See our User Agreement and Privacy Policy. Slides. Machine learning books; Trevor Hastie, Rob Tibshirani, and Jerry Friedman, Elements of Statistical Learning, Second Edition, Springer, 2009.