"Prediction: Machine Learning and Statistics" is a Course materials drawn from MIT Open Learning and catalogued under Computer Science for Undergraduate / College. From the source: Prediction is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning and statistics, and more generally, data mining. Machine… Slide Collection preserves the upstream link, the original creator credit and the licensing terms; download the file to use it in a classroom, study group or revision plan.
About this presentation
Prediction is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning and statistics, and more generally, data mining. Machine learning and statistical methods are used throughout the scientific world for their use in handling the “information overload” that characterizes our current digital age. Machine learning developed from the artificial intelligence community, mainly within the last 30 years, at the same time that statistics has made major advances due to the availability of modern computing. However, parts of these two fields aim at the same goal, that is, of prediction from data. This course provides a selection of the most important topics from both of these subjects.
How to study this deck
Computer-science slides are deceptively dense. Code snippets and diagrams collapse hours of design decisions into a few lines, so resist the urge to skim. Run the snippets locally, change one variable, and observe what breaks.
Undergraduate viewers should treat this as a scaffolding for deeper reading — the slides outline the territory, but the textbook chapters and primary sources remain the actual content.
Five questions to test your understanding
- What is the single most important claim on the first three slides, and what evidence is offered for it?
- Which slide could you remove without losing the argument? Which slide is load-bearing?
- Where does the deck switch from definitions to applications? Mark that transition.
- What would a student who already disagreed with the conclusion need to see to be convinced?
- Which two slides, if combined, would give the clearest one-slide summary of the whole deck?
Where this deck fits in the wider catalogue
Slide Collection classifies this presentation under Computer Science, alongside other openly-licensed material in the same subject. If you are preparing a unit at the Undergraduate / College level, the dedicated combined Computer Science · Undergraduate / College page is the fastest way to find adjacent decks with the same audience in mind.
Citation & reuse
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