Probability and Statistics for Deep Learning

Probability and Statistics for Deep Learning

Deep Learning is often called “Statistical Learning” and approached by many experts as statistical theory of the problem of the function estimation from a given collection of data. This course will introduce fundamental concepts of probability theory and statistics. It will cover many important algorithms and modelling used in supervised learning of neural networks. In addition, the course will introduce tools and underlying mathematical concepts of data interpretation that work with specific models of neural networks. Upon completion of this course you will have acquired the background in probability and statistics necessary for Machine Learning, and have the ability to use TensorFlow to create and train neural networks for specific practical problems.

Topics Include:

• Revise the basic probability theory and learn common data distributions used in machine learning
• Bayesian concept learning
• Gaussian models, (mixture model)
• Undirected graphical model
• Linear and Logistic Regression
• Support Vector Machines for classification, (Kernels)
• Feedforward artificial neural network, multilayer perceptron (MLP)
• Learn how to use Python/R in TensorFlow for Deep NN

Software: TensorFlow, an open source software library for high performance numerical computation.

Course typically offered: Online during our Spring and Fall academic quarters.

Prerequisites: Basic knowledge of Linear Algebra - concept of vectors and matrices.

Next steps: Upon completion, consider additional coursework in our specialized certificate in Machine Learning Methods to continue learning.

More information: For more information about this course, please contact unex-techdata@ucsd.edu.

Course Number: CSE-41305
Credit: 3.00 unit(s)
Related Certificate Programs: Machine Learning Methods

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10/5/2020 - 12/5/2020

$725

Online

Closed

• SECTION ID:

  151439

• CLASS TYPE:

  Online, Web-based instruction

• INSTRUCTOR:
  

  Aleksic, Bilyana

  Bilyana Aleksic is senior staff hardware engineer with more than 20 years of experience in complex chip SOC development. She received her master's in control systems from Faculty of Electrical Engineering University of Belgrade, former Yugoslavia, and was an honored student of Mathematical Gymnasium, winning many world Mathematical Olympiad Competitions. Aleksic spent 15 years at Canadian tech company ATI Technologies where she worked on implementation of graphics CPU chips. After she moved to California she spent 7 years in mobile chips implementation and verification methodology development. During the past year she shifted her focus to machine learning for medical applications.

• TEXTBOOKS:

  No information available at this time.

• POLICIES:

  No refunds after: 10/11/2020.
  

• NOTE:

• DATE & LOCATION:

  10/5/2020 - 12/5/2020
  extensioncanvas.ucsd.edu
  You will have access to your course materials on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.