• Duración:
    14 semanas
  • Dedicación:
    10–14 horas por semana
  • Precio:

    GRATIS
    Agregar un Certificado Verificado por $300 USD

  • Institución
  • Tema:
  • Nivel:
    Advanced
  • Idioma:
    English
  • Transcripción de video:
    English

Prerrequisitos

  • 6.00.1x or proficiency in Python programming 
  • 6.431x or equivalent probability theory course
  • College-level single and multi-variable calculus
  • Vectors and matrices

Sobre este curso

Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk.

As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control. 

In this course, students will learn about principles and algorithms for turning training data into effective automated predictions.  We will cover:
  • Representation, over-fitting, regularization, generalization, VC dimension;
  • Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning;
  • On-line algorithms, support vector machines, and neural networks/deep learning.
Students will implement and experiment with the algorithms in several Python projects designed for different practical applications.

This course is part of the MITx MicroMasters Program in Statistics and Data Science. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit https://micromasters.mit.edu/ds/.

If you have specific questions about this course, please contact us at [email protected].

Lo que aprenderás

  • Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning
  • Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models
  • Choose suitable models for different applications
  • Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering.
Lectures:
  • Introduction
  • Linear classifiers, separability, perceptron algorithm
  • Maximum margin hyperplane, loss, regularization
  • Stochastic gradient descent, over-fitting, generalization
  • Linear regression
  • Recommender problems, collaborative filtering
  • Non-linear classification, kernels
  • Learning features, Neural networks
  • Deep learning, back propagation
  • Recurrent neural networks
  • Recurrent neural networks
  • Generalization, complexity, VC-dimension
  • Unsupervised learning: clustering
  • Generative models, mixtures
  • Mixtures and the EM algorithm
  • Learning to control: Reinforcement learning
  • Reinforcement learning continued
  • Applications: Natural Language Processing
Projects:
  • Automatic Review Analyzer
  • Digit Recognition with Neural Networks
  • Reinforcement Learning

Conoce a tus instructores

Regina Barzilay
Delta Electronics Professor in the Department of Electrical Engineering and Computer Science
MIT
Tommi Jaakkola
Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society
MIT
Karene Chu
Lecturer and Research Scientist
Massachusetts Institute of Technology

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Preguntas frecuentes

Should you have further inquiries, go to micromasters.mit.edu/ds and use the "Contact us" button.

¿Quién puede hacer este curso?

Lamentablemente, las personas de uno o más de los siguientes países o regiones no podrán registrarse para este curso: Irán, Cuba y la región de Crimea en Ucrania. Si bien edX consiguió licencias de la Oficina de Control de Activos Extranjeros de los EE. UU. (U.S. Office of Foreign Assets Control, OFAC) para ofrecer nuestros cursos a personas en estos países y regiones, las licencias que hemos recibido no son lo suficientemente amplias como para permitirnos dictar este curso en todas las ubicaciones. edX lamenta profundamente que las sanciones estadounidenses impidan que ofrezcamos todos nuestros cursos a cualquier persona, sin importar dónde viva.