[CMU] 10 – 301/601 – Spring 2020 Lecture 24 Reinforcement Learning and Q Learning | cmu learning | วิดีโอที่ดีที่สุด

[CMU] 10 301/601 Spring 2020 Lecture 24 Reinforcement Learning and Q Learning

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Course Overview:
Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., programs that learn to recognize human faces, recommend music and movies, and drive autonomous robots). This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as Bayesian networks, decision tree learning, Support Vector Machines, statistical learning methods, unsupervised learning, and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, marginbased learning, and Occam’s Razor. Programming assignments include handson experiments with various learning algorithms. This course is designed to give a graduatelevel student a thorough grounding in the methodologies, technologies, mathematics, and algorithms currently needed by people who do research in machine learning.

10301 and 10601 are identical. Undergraduates must register for 10301 and graduate students must register for 10601.

Learning Outcomes: By the end of the course, students should be able to:

Implement and analyze existing learning algorithms, including wellstudied methods for classification, regression, structured prediction, clustering, and representation learning
Integrate multiple facets of practical machine learning in a single system: data preprocessing, learning, regularization and model selection
Describe the formal properties of models and algorithms for learning and explain the practical implications of those results
Compare and contrast different paradigms for learning (supervised, unsupervised, etc.)
Design experiments to evaluate and compare different machine learning techniques on realworld problems
Employ probability, statistics, calculus, linear algebra, and optimization in order to develop new predictive models or learning methods
Given a description of an ML technique, analyze it to identify:
(1) the expressive power of the formalism;
(2) the inductive bias implicit in the algorithm;
(3) the size and complexity of the search space;
(4) the computational properties of the algorithm:
(5) any guarantees (or lack thereof) regarding termination, convergence, correctness, accuracy, or generalization power.

Lecture 10.1: Fusion, colearning, and new trend (Multimodal Machine Learning, CMU)

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In this webinar, learn about CMU’s worldrenowned Department of Statistics and Data Science and how employers can connect and engage with the department and students.

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In it, we discuss active learning on the token level and sequence level, as well as factors of human effort to consider when doing active learning.

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MechE Department Head Allen Robinson and several other faculty members explain how artificial intelligence and machine learning affect their research and the curricula they teach, both at the graduate and undergraduate level.

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CMU’s online RNtoBSN program builds on your nursing foundation and broadens your expertise in nursing leadership, advanced health assessment, culture and diversity in patient care, and the overarching concepts of global and population health.