Start Date: 11/22/2020
Course Type: Specialization Course |
Course Link: https://www.coursera.org/specializations/machine-learning
This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
Machine Learning Foundations: A Case Study Approach
Machine Learning: Regression
Machine Learning: Classification
Machine Learning: Clustering & Retrieval
Build Intelligent Applications. Master machine learning fundamentals in four hands-on courses. Machine Learning Specialization The Machine Learning Specialization aims to offer university students a high-level overview of the field of Machine Learning, with an emphasis on the practical application of machine learning algorithms in real applications. Special attention is given to design of hidden layers to improve performance over a fixed-grid cell, evaluate the cost-performance tradeoff between cost-optimization and cost-stability, and explore the design of algorithms for cost-reproducibility. The Specialization is intended for students who intend to work as industry experts in the design, implementation and evaluation of algorithms for cost-reproducibility and cost-optimal performance. It is also intended for students who have basic knowledge of computer science, basic mathematics, and machine learning, as well as for those who want to explore a career path in machine learning.Introduction Probabilistic Modeling Cost Performance Cost Optimal Performance Monte Carlo Regression The main focus of this course is the regression of a continuous variable with mean and standard deviation. We will use Monte Carlo simulation to achieve this. We will also introduce random variables and a continuous variable with mean and standard deviation. We will then use regression weights to evaluate the association between a variable's regression slope and the test statistic. Lastly, we will introduce a random variable with mean and standard deviation for an independent variable. The basic idea of this course is to learn the basic
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Active learning (machine learning) | Recent developments are dedicated to hybrid active learning and active learning in a single-pass (on-line) context, combining concepts from the field of Machine Learning (e.g., conflict and ignorance) with adaptive, incremental learning policies in the field of Online machine learning. |
Machine learning | Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves `rules’ to store, manipulate or apply, knowledge. The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learners that commonly identify a singular model that can be universally applied to any instance in order to make a prediction. Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems. |
Machine learning | Some statisticians have adopted methods from machine learning, leading to a combined field that they call "statistical learning". |
Machine learning | Machine learning tasks are typically classified into three broad categories, depending on the nature of the learning "signal" or "feedback" available to a learning system. These are |
Machine learning | Another categorization of machine learning tasks arises when one considers the desired "output" of a machine-learned system: |
Machine learning | Machine Learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use, thus digitizing cultural prejudices. Responsible collection of data thus is a critical part of machine learning. |
Adversarial machine learning | Adversarial machine learning is a research field that lies at the intersection of machine learning and computer security. It aims to enable the safe adoption of machine learning techniques in adversarial settings like spam filtering, malware detection and biometric recognition. |
Machine learning | Software suites containing a variety of machine learning algorithms include the following : |
Outline of machine learning | [[Category:Artificial intelligence|Machine learning]] |
Machine learning | Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component (e.g. typically a genetic algorithm) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning). They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions. |
Machine learning | Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies. |
Quantum machine learning | Quantum machine learning is an emerging interdisciplinary research area at the intersection of quantum physics and machine learning. One can distinguish four different ways of merging the two parent disciplines. Quantum machine learning algorithms can use the advantages of quantum computation in order to improve classical methods of machine learning, for example by developing efficient implementations of expensive classical algorithms on a quantum computer. On the other hand, one can apply classical methods of machine learning to analyse quantum systems. Most generally, one can consider situations wherein both the learning device and the system under study are fully quantum. |
Machine learning | Machine learning and statistics are closely related fields. According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics. He also suggested the term data science as a placeholder to call the overall field. |
International Conference on Machine Learning | The International Conference on Machine Learning (ICML) is the leading international academic conference in machine learning, attracting annually more than 2000 participants from all over the world. It is supported by the International Machine Learning Society (IMLS). |
Machine learning | Among other categories of machine learning problems, learning to learn learns its own inductive bias based on previous experience. Developmental learning, elaborated for robot learning, generates its own sequences (also called curriculum) of learning situations to cumulatively acquire repertoires of novel skills through autonomous self-exploration and social interaction with human teachers and using guidance mechanisms such as active learning, maturation, motor synergies, and imitation. |
Logic learning machine | Logic Learning Machine is implemented in the Rulex suite. |
Machine learning | Leo Breiman distinguished two statistical modelling paradigms: data model and algorithmic model, wherein 'algorithmic model' means more or less the machine learning algorithms like Random forest. |
Active learning (machine learning) | Active learning is a special case of semi-supervised machine learning in which a learning algorithm is able to interactively query the user (or some other information source) to obtain the desired outputs at new data points. In statistics literature it is sometimes also called optimal experimental design. |
Machine learning | The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The bias–variance decomposition is one way to quantify generalization error. |
Machine learning | Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959, gives "computers the ability to learn without being explicitly programmed." Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or unfeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach, optical character recognition (OCR), learning to rank and computer vision. |