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i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI)

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Stanford University

Andrew Ng

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a linear approach to modelling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables).

Statistics Supervised Learning

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Methods of machine learning, fields of application and tools

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Hasso-Plattner-Institut f?r Digital Engineering gGmbH

Dr. Damian Borth Johannes Melzer

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the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.

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Wikipedia

Most popular optimization strategy, used to minimize some function by iteratively moving in the direction of steepest descent

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Wikipedia

a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. An optimization problem seeks to minimize a loss function

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Videos, assesments, assignments, Microsoft

Exchange

Community of data scientists, Competition, Online Education

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NVIDIA

Artificial Intelligence Deep Learning

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Open-source, numerical computation

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Torch, natural language processing, CUDA

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new technique, high dimensional data

Geoffrey Hinton

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Human-centered AI and autonomous vehicles

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MIT

Deep Learning Artificial Intelligence

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Practical introduction to machine learning with TensorFlow APIs

video lectures, real-world case studies, hands-on practice exercises.

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Other

Machine Learning library

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Wikipedia

Education

Wikipedia

Machine learning, recommendation system

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Blog

Natural Language Processing

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Blog

Shawn Tan

Software Engineering Artificial Intelligence

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Person

Study of kernel methods for extracting regularities from possibly high-dimensional data

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Sebastian Raschka Vahid Mirjalili

Community of data scientists, Competition, Online Education

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Natural Language Processing

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Open-source, numerical computation

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Torch, natural language processing, CUDA

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i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI)

*Upvote*
*
*

Methods of machine learning, fields of application and tools

*Upvote*
*
*

Videos, assesments, assignments, Microsoft

*Upvote*
*
*

Practical introduction to machine learning with TensorFlow APIs

*Upvote*
*
*

Human-centered AI and autonomous vehicles

*Upvote*
*
*

Study of kernel methods for extracting regularities from possibly high-dimensional data

*Upvote*
*
*

a linear approach to modelling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables).

*Upvote*
*
*

the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.

*Upvote*
*
*

guests, experts

*Upvote*
*
*

Human-centered AI and autonomous vehicles

*Upvote*
*
*

Natural Language Processing

*Upvote*
*
*

*Upvote*
*
*

Open-source, numerical computation

*Upvote*
*
*

Torch, natural language processing, CUDA

*Upvote*
*
*

Machine Learning library

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*
*

i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI)

*Upvote*
*
*

Methods of machine learning, fields of application and tools

*Upvote*
*
*

Most popular optimization strategy, used to minimize some function by iteratively moving in the direction of steepest descent

*Upvote*
*
*

a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. An optimization problem seeks to minimize a loss function

*Upvote*
*
*

Videos, assesments, assignments, Microsoft

*Upvote*
*
*

new technique, high dimensional data

*Upvote*
*
*

Practical introduction to machine learning with TensorFlow APIs

*Upvote*
*
*

*Upvote*
*
*

Machine learning, recommendation system

*Upvote*
*
*

Research insights

*Upvote*
*
*

Study of kernel methods for extracting regularities from possibly high-dimensional data

*Upvote*
*
*

*Upvote*
*
*

Community of data scientists, Competition, Online Education

*Upvote*
*
*

Questions and answers

*Upvote*
*
*