This class was created by Brainscape user Simon Sardorf. Visit their profile to learn more about the creator.

Decks in this class (12)

Lecture 1: Fundamentals of Statistics: Probability and Distribution
No deck description has yet been added by the author.
16  cards
Lecture 2 - Introduction to Machine Learning
No deck description has yet been added by the author.
46  cards
Lecture 3 - Unsupervised Machine Learning
No deck description has yet been added by the author.
34  cards
Lecture 4 - Supervised Machine Learning: KNN and Regression
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25  cards
Lecture 5 - Dimensionality Reduction - Principal Component Analysis, Linear Discrimination Analysis, Singular Value Decomposition
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25  cards
Lecture 6 - Support Vector Machines, Decision Tree, Ensemble, Hypothesis Testing
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39  cards
Lecture 7 - Outlier Detection, Feature Selection, Similar Items, Recommender Systems, Naive Bayes Classifiers, Class Imbalance
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45  cards
Lecture 8 - (Stochastic) Gradient Descent, Regularization, Artificial Neural Networks, Perceptron
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53  cards
Lecture 9 - Cloud Computing, Big Data, Tensor-flow, Recurrent Neural Networks, Distributed Deep Learning
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32  cards
Lecture 12: Philosophy and Ethics of AI
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8  cards
Lecture 10 - LSTM - CNN -GAN - BatchNorm
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30  cards
Lecture 11 - Hyper-parameter Optimization - Deep Generative Models - Autoencoders
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32  cards

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machine learning

  • Class purpose General learning

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