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Machine Learning (2015, University of Oxford). Instructor: Professor Nando de Freitas. Machine learning techniques enable us to automatically extract features from data so as to solve predictive tasks, such as speech recognition, object recognition, machine translation, question-answering, anomaly detection, medical diagnosis and prognosis, automatic algorithm configuration, personalization, robot control, time series forecasting, and much more. Learning systems adapt so that they can solve new tasks, related to previously encountered tasks, more efficiently.

FREE
This course includes
Hours of videos

444 years, 4 months

Units & Quizzes

16

Unlimited Lifetime access
Access on mobile app
Certificate of Completion

The course focuses on the exciting field of deep learning. By drawing inspiration from neuroscience and statistics, it introduces the basic background on neural networks, back propagation, Boltzmann machines, autoencoders, convolutional neural networks and recurrent neural networks. It illustrates how deep learning is impacting our understanding of intelligence and contributing to the practical design of intelligent machines.
(from cs.ox.ac.uk)

Course Currilcum

  • Lecture 01 – Introduction Unlimited
  • Lecture 02 – Linear Prediction Unlimited
  • Lecture 03 – Maximum Likelihood Unlimited
  • Lecture 04 – Regularizers, Basis Functions and Cross-validation Unlimited
  • Lecture 05 – Regularizers, Basis Functions and Cross-validation (cont.) Unlimited
  • Lecture 06 – Optimisation Unlimited
  • Lecture 07 – Logistic Regression Unlimited
  • Lecture 08 – Modular Backpropagation, Logistic Regression and Torch Unlimited
  • Lecture 09 – Neural Networks and Modular Design in Torch Unlimited
  • Lecture 10 – Convolutional Neural Networks Unlimited
  • Lecture 11 – Max-margin Learning, Transfer and Memory Networks Unlimited
  • Lecture 12 – Recurrent Neural Networks and LSTMs Unlimited
  • Lecture 13 – Generation Sequences with Recurrent Neural Networks by Alex Graves Unlimited
  • Lecture 14 – Variational Autoencoders and Deep Recurrent Attentive Writers by Karol Gregor Unlimited
  • Lecture 15 – Reinforcement Learning with Direct Policy Search Unlimited
  • Lecture 16 – Reinforcement Learning and Neuro-dynamic Programming Unlimited