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 in Fall, Winter, Spring & Summer (each quarter).
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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1/8/2019 - 3/9/2019
$650
Online
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CLASS TYPE:
Online, Web-based instruction
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.
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TEXTBOOKS:
No information available at this time.
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POLICIES:
No refunds after: 1/15/2019.
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1/8/2019 - 3/9/2019
ucsdextension.blackboard.com
You will have access to your course on the published start date.
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4/9/2019 - 6/8/2019
$650
Online
-
-
-
CLASS TYPE:
Online, Web-based instruction
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.
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TEXTBOOKS:
No information available at this time.
-
POLICIES:
No refunds after: 4/16/2019.
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4/9/2019 - 6/8/2019
ucsdextension.blackboard.com
You will have access to your course on the published start date.
There are no sections of this course currently scheduled. Please contact the Science & Technology department at 858-534-3229 or unex-sciencetech@ucsd.edu for information about when this course will be offered again.