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CY-701 Machine Learning Notes

5 notes for Machine Learning, unit by unit.

Unit 1 · Short notes Introduction to Machine Learning introduction to machine learning · scope and limitations of machine learning · regression · probability · statistics · linear algebra for machine learning
Unit 2 · Short notes Deep Learning Fundamentals linearity vs non‑linearity · activation functions (sigmoid, ReLU, etc.) · weights and bias · loss function · gradient descent · multilayer network
Unit 3 · Short notes Convolutional Neural Networks convolutional neural network (CNN) · flattening · subsampling · padding · stride · convolution layer
Unit 4 · Short notes Recurrent Neural Networks and Reinforcement Learning recurrent neural network (RNN) · long short‑term memory (LSTM) · gated recurrent unit (GRU) · translation models · beam search and beam width · BLEU score
Unit 5 · Short notes Support Vector Machines and Applications support vector machines (SVM) · Bayesian learning · application of machine learning in computer vision · application of machine learning in speech processing · application of machine learning in natural language processing · case study: ImageNet competition

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