AI News, advanced topics in artificial intelligence

Machine Learning - Intermediate Level 1 on 1 Highly Personalized

Duration Hours: 8 + 1 HOURS ONSITE TRAINING (5 Sessions: 2Hrs + 2Hrs + 2Hrs + 2Hrs + 1Hrs) Method: 1 on 1 Schedule is tentative;

This course will teach you practical ways to build your own machine learning solutions.Machine learning is a form of data analysis that gives computers the ability to learn and processinformation with little human intervention.

From automatic recommendations of which movies to watch, to whatfood to order or which products to buy, to personalized online radio and recognizing your friendsin your photos, many modern websites and devices have machine learning algorithms at their core.When you look at a complex website like Facebook, Amazon, or Netflix, it is very likely that everypart of the site contains multiple machine learning models.

In this course, students will knowthe methods and tools widely applied to the field of machine learning: linear models for regression andclassification, clustering methods, working with text data, neural networks, reinforcement learning, andother advanced topics.

Instructor Pooja Umathe is an Aspiring Data Scientist with strong Analytical background and 3+ years of experience using predictive modeling, data processing, machine learning, and data mining algorithms to solve challenging business problems.

Advanced Topics in Self-Adaptive Systems: Online Machine Learning (Wintersemester 2019/2020)

The important concern for modern software systems is to become more cost-effective, while being versatile, flexible, resilient,self-healing , energy-efficient, customizable, configurable and self-optimizing when reacting to runtime changes.

This is very challenging for two reasons: (1) data is dynamically produced by SAS in large amounts and (2) there is no obvious criteria to determine how to sample this data for training and testing.

LL tackles the fundamental problem of evolving machine learning models by relying on four system requirements [3]: learn on the job (online), discover new prediction tasks, update knowledge base (strengthen or remove beliefs), and apply current knowledge to new tasks (transfer learning).

LL has been successfully applied in combination with machine learning algorithms [3], for instance, reinforcement learning, neural networks, topic modeling, and sentiment analysis.

Different optimization frameworks also allow to steer LL, for instance, Bayesian optimization [4,5], interactive optimization [6,7], and interactive machine learning [8].

Second, system-wide knowledge is distributed among the agents, entailing that advanced mechanisms should be used for efficient knowledge sharing and acquisition between agents [10,11] To address these issues, CSAS agentsmust be enhanced with \emph{learning capabilities}, allowing them both to instantiate learning models using the knowledge acquired from observing the environment and their peers,and to refine these models by assessing the outcomes of their actions[12].

In this seminar we are interested in studying the common practice and learning techniques that best fit the learning-enabled collective self-adaptive systems and the variation of their applications.

Andrew Ng: "Advanced Topics + Research Philosophy / Neural Networks: Representation"

Graduate Summer School 2012: Deep Learning, Feature Learning "Advanced Topics + Research Philosophy / Neural Networks: Representation" Andrew Ng, ...

Advanced Topics in Artificial Intelligence 13

Advanced Topics in Artificial Intelligence مباحث پیشرفته در هوش مصنوعی

Reinforcement Learning 8: Advanced Topics in Deep RL

Advanced Topics in Artificial Intelligence 06

Advanced Topics in Artificial Intelligence مباحث پیشرفته در هوش مصنوعی.

Advanced Topics in Artificial Intelligence 07

Advanced Topics in Artificial Intelligence مباحث پیشرفته در هوش مصنوعی

Advanced Topics in Artificial Intelligence 11

Advanced Topics in Artificial Intelligence مباحث پیشرفته در هوش مصنوعی

Advanced Topics in Artificial Intelligence 01

Advanced Topics in Artificial Intelligence مباحث پیشرفته در هوش مصنوعی