Machine Learning with Python: from Linear Models to Deep Learning

Machine Learning with Python: from Linear Models to Deep Learning

Closed
Course
en
English
150 h
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More info
  • 15 Sequences
  • Advanced Level
  • Starts on September 3, 2023
  • Ends on December 18, 2023

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Course details

Syllabus

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
  • 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

Prerequisite

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

Instructors

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
Digital Learning Scientist and Research Scientist
Massachusetts Institute of Technology

Editor

MIT is a world-class educational institution where teaching and research — with relevance to the practical world as a guiding principle — continue to be its primary purpose.

MIT is independent, coeducational, and privately endowed. Its five schools and one college encompass numerous academic departments, divisions and degree-granting programs, as well as interdisciplinary centers, laboratories and programs whose work cuts across traditional departmental boundaries.

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