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It appears that you have an ad-blocker running. By whitelisting SlideShare on your ad-blocker, you are supporting our community of content creators. Successfully reported this slideshow. Your SlideShare is downloading. Lecture 1: What is Machine Learning? Marina Santini. Next SlideShares. You are reading a preview. Activate your 30 day free trial to continue reading. Continue for Free. Upcoming SlideShare. Machine Learning. Lecture 1: Introduction to the Course Practical Information.

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Computational Linguist, PhD. More Related Content Slideshows for you Introduction to machine learning and deep learning. Machine Learning: Applications, Process and Techniques.

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Analysis of Wind Energy Fatalities. Marina Santini santinim stp. What is Machine Learning? This covers cases like “all the while” or “in a while”, where “while” should be tagged as a noun rather than its more common use as a preposition many rules are more general. The training material is a collection of tokenized sentences where each token has the assigned part-of-speech tag.

The accuracy can be measured on a test dataset or via cross validation. Sometimes this is informally called a “top-down” approach. We might begin with thinking up a theory about our topic of interest. We then narrow that down into more specific hypotheses that we can test. This ultimately leads us to be able to test the hypotheses with specific data — a confirmation or not of our original theories. Informally, we sometimes call this a “bottom up” approach. In inductive reasoning, we begin with specific observations the data and measures, begin to detect patterns and regularities, formulate some tentative hypotheses that we can explore, and finally end up developing a general model.

Machine Learning is based on The whole idea is to replace the “human writing code” with a “human supplying data” and then let the system figure out what it is that the person wants to do by looking at the examples.

Why learning? Why is ML so fashionable? M Lecture 1: What is Machine Learning? He also suggested the term data science as a placeholder to call the overall field. During this process machine Learning algorithms are used. Example: Spam Filter by Andrew Y. Classifying emails as spam or not spam 2. Watching you label emails as spam or not spam. None of the above—this is not a machine learning problem. Spam Filter by Andrew Y. Underfitting is often a result of an excessively simple model.

Data: The iris dataset Three components: 1. Models Models are theoretical assumptions — decision trees, naive bayes, perceptron, etc. Given: — Domain X: descriptions — Domain Y: predictions — H: hypothesis space; the set of all possible hypotheses — h: target hypothesis — Idea: to extrapolate observed y’s over all X.

Trees that place high information gain attributes close to the root are preferred over those that do not. Lecture 3: Basic Concepts of ML 33 You just clipped your first slide! Clipping is a handy way to collect important slides you want to go back to later.

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Introduction to machine learning – ppt video online download

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Go is year old 19 what is machine learning ppt 19 chinese game which has more possible positions than there are atoms in the universe The computer system was specifically developed to answer questions on the quiz show Jeopardy!

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CH. 1: Introduction What is Machine Learning Example: – ppt download – Check these out next

 
 
Machine learning is an application of artificial intelligence that involves algorithms and data that automatically analyse and make decision. Definition by Tom Mitchell (): Machine Learning is the study of algorithms that • improve their performance P • at some task T • with experience E. A well-.