Course: Learning Machine Learning 2018

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Agustin González

Agustin González

Course: Learning Machine Learning 2018

  • Modalidad de impartición
    El modo de enseñanza es presencial.
  • Número de horas
    Son 48 horas de curso.
  • Titulación oficial
    El centro entrega un certificado de estudios.
  • Valoración del programa
    El Course: Learning Machine Learning 2018 es un programa de estudios impartido por la Universidad del Rosario (Summer School) en donde el alumno aprenderá sobre el machine learning: una disciplina que estudia la inteligencia artificial, permitiendo crear sistemas automáticos, crear algoritmos y es capaz de predecir comportamientos futuros.
  • Dirigido a
    Está pensado para personas que, sin necesidad de tener experiencia en el tema, quiera aprender más sobre estadísticas, programación, machine learning.
  • Empleabilidad
    Es muy útil para realizar estudios de mercado y planificar estrategias comerciales a futuro.

Comentarios sobre Course: Learning Machine Learning 2018 - Presencial - Colombia - Exterior

  • Contenido
    Course: Learning Machine Learning 2018.

    Language: English.

    Call for projects.

    This is a form that will allow you to tell us about a possible project that you have in mind for which Machine Learning can be useful. 
      
    Description.

    Learning Machine Learning 2018 (LML18) is a 6-day summer school organized by the Department of Applied Mathematics and Computer Science, Universidad del Rosario with the support of Universidad de Los Andes and the Institute for Applied Computational Science (IACS) of Harvard University. LML18 is an event that will bring together beginners and experts in Machine Learning (ML) and Data Science (DS) in a multilevel school that will cover basic concepts in these areas, but will also provide multidisciplinary spaces for participants to develop projects based on real datasets from academia, industry, or the public sector. During the first four days, basic courses and intermediate workshops will be offered on modern methodologies of ML, such as supervised and unsupervised classification, regression, and the basics of artificial neural networks. The last two days will be hack days in which participants will have the chance to apply these methods to solve real problems provided by companies, research groups, or the participants themselves.
      
    Target public.

    LML18 is a multilevel school intended for a wide audience of people interested in ML. This includes those with limited or very basic experience on statistics, python programming and machine learning, those who own large and complex datasets whose analysis requires state-of-the-art methods, and also those with intermediate to advanced experience in ML who would like to update their tools and create collaborations. Basic knowledge of probability, statistics and Python is desirable, but even those without this experience should be able to follow the course (as long as they like data, statistics, and programming).
      
    Methodology.

    Each of the first four days of LML18 will have two plenary lectures (with exercises), and two practical unconferences or labs to practice with specific tools and problems. The last two days will be hack days, in which participants will work on specific projects involving real data. Multilevel working groups will be formed to work on those specific problems. Public and private institutions will also have a chance to present some of their specific data problems and challenges.

    Schedule.

    • Dates: July 23 to July 28, 2018
    • Schedule: Monday to Saturday 8:00 a.m. – 6:00 p.m.
    • Venue: Claustro, Universidad del Rosario
    • Duration: 48 hours
    • Type: Course
    • Credits: 3 UR credits (5.1 ECTS)
    • Language: English
    • Organized by: Department of Applied Mathematics and Computer Science, Universidad del Rosario – Universidad de los Andes - Institute for Applied Computational Science at Harvard University.
    Contents:   
     
    CONFERENCES.

    Introduction to Machine learning and probability.

    Topics: 

    • Introduction to ML. 
    • Laws of probability. 
    • Bayes rule. 
    • Distributions. 
    • Maximum likelihood estimation.
    Date: 23/07/2018.
    Schedule:  8:00 - 10:00 | 10:30 - 12:30.

    Optimization.

    Topics:

    • Introduction to optimization. Convexity.
    • Stochastic gradient descent.
    • Newton’s method.
    • Constrained optimization.
    Date: 24/07/2018
    Schedule:  8:00 - 10:00 | 10:30 - 12:30.

    Machine learning I.

    Topics:

    • Classification. Generative models. Logistic regression.
    • Support vector machines.
    • Maximum margin. Nearest neighbors.
    • Maximum margin. Nearest neighbors.
    Date: 25/07/2018
    Schedule: 8:00 - 10:00 | 10:30 - 12:30.

    Machine learning II.

    Topics:

    • Validation
    • Cross-validation
    • Unsupervised methods
    • Clustering
    Date: 26/07/2018
    Schedule: 8:00 - 10:00 | 10:30 - 12:30.

    UNCONFERENCES.

    Unconferences day I.

    Topics: Python I: Basics
    Date: 23/07/2018
    Schedule: 2:00 p.m. | 3:30 p.m.
     
    Topics: Introduction to MCM (Metropolis)
    Date: 23/07/2018
    Schedule: 2:00 p.m. | 3:30 p.m.
     
    Topics: Distributions in the real world
    Date: 23/07/2018
    Schedule: 2:00 p.m. | 3:30 p.m.
     
    Unconferences day II.

    Topics: Python II: Theano, scikit-learn.
    Date: 24/07/2018.
    Schedule: 2:00 p.m. | 3:30 p.m.

    Topics: Simulated annealing
    Date: 24/07/2018
    Schedule: 2:00 p.m. | 3:30 p.m.
     
    Topics: Genetic algorithms
    Date: 24/07/2018
    Schedule: 2:00 p.m. | 3:30 p.m.
     
    Topics: Linear regression
    Date: 24/07/2018
    Schedule: 2:00 p.m. | 3:30 p.m.
     
    Unconferences day III.

    Topics: Support vector machines
    Date: 25/07/2018
    Schedule: 2:00 p.m. | 3:30 p.m.

    Topics: Classifying the MNIST dataset
    Date: 25/07/2018
    Schedule: 2:00 p.m. | 3:30 p.m.
     
    Topics: Bayesian linear regression
    Date: 25/07/2018
    Schedule: 2:00 p.m. | 3:30 p.m.
     
    Topics: Introduction to neural networks
    Date: 25/07/2018
    Schedule: 2:00 p.m. | 3:30 p.m.
     
    Unconferences day IV.

    Topics: Regularization
    Date: 25/07/2018
    Schedule: 2:00 p.m. | 3:30 p.m.

    Topics: PyMC3
    Date: 25/07/2018
    Schedule: 2:00 p.m. | 3:30 p.m.

    Topics: K-means
    Date: 25/07/2018
    Schedule: 2:00 p.m. | 3:30 p.m.
     
    Topics: Model selection: regularization
    Date: 25/07/2018
    Schedule: 2:00 p.m. | 3:30 p.m.
     
    Topics: Bayesian inference with PyMC3
    Date: 25/07/2018
    Schedule: 2:00 p.m. | 3:30 p.m.

    *May be subject to change.

    Discounts:

    The price is subject to change depending on the exchange rate.
     
    Early bird discount: 10% discount on the course fees until July 8, 2018.

    Rosario community discount: A 10% discount on enrollment fees. This may be received in addition to the early bird discount.

    Discount for students, professors and graduates from international institutions: 10% discount on the course fees, which can be accumulated and/or combined with the benefit for early bird discount.

    General Terms and Conditions:

    The University reserves the right to cancel a course or program if the minimum financial resources needed to open the course or program are not met. In such event, the University will refund 100% of the amount paid by the students in the currency of origin by electronic transfer (wire transfer) to the bank account they have provided. The transfer will be made within five business days following the date that the participant's bank account information was received.

    In the event that a participant decides not to take the course or program and has already paid the corresponding enrollment amount, provided the course has not commenced, the participant may request the refund of his/her money. In this case, the University will make an electronic transfer in the amount of 90% of the amount that was originally received; 10% of which will be retained for the reimbursement of administrative and banking expenses incurred by the university. The refund will be made in the original payment’s currency of origin within 8 business days following receipt of the request to withdraw, cancel and receive a refund.

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