Machine Learning Fundamentals
The University of California, San Diego
Edx
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assignment Level : Advanced
chat_bubble_outline Language : English
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Key information

credit_card Free access
verified_user Fee-based Certificate
timer 80 hours in total

About the content

Do you want to build systems that learn from experience? Or exploit data to create simple predictive models of the world?

In this course, part of the Data Science MicroMasters program, you will learn a variety of supervised and unsupervised learning algorithms, and the theory behind those algorithms.

Using real-world case studies, you will learn how to classify images, identify salient topics in a corpus of documents, partition people according to personality profiles, and automatically capture the semantic structure of words and use it to categorize documents.

Armed with the knowledge from this course, you will be able to analyze many different types of data and to build descriptive and predictive models.

All programming examples and assignments will be in Python, using Jupyter notebooks.

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Prerequisite

  1. The previous courses in the MicroMasters program: DSE200x and DSE210x
  2. Undergraduate level education in:
    • Multivariate calculus
    • Linear algebra
       

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Syllabus

  • Classification, regression, and conditional probability estimation
  • Generative and discriminative models
  • Linear models and extensions to nonlinearity using kernel methods
  • Ensemble methods: boosting, bagging, random forests
  • Representation learning: clustering, dimensionality reduction, autoencoders, deep nets
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Intructors

Sanjoy Dasgupta
Professor of Computer Science and Engineering
UC San Diego

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The University of California, San Diego
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