CS-601 Machine Learning Notes
5 notes for Machine Learning, unit by unit.
Notes for CS-601
- Unit 1 · Short notes Introduction to Machine Learning Introduction to machine learning · Scope and limitations · Regression · Probability · Statistics and linear algebra for machine learning · Convex optimization
- Unit 2 · Short notes Deep Learning Foundations 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 · Flattening · Subsampling · Padding · Stride · Convolution layer
- Unit 4 · Short notes Recurrent Neural Networks and Reinforcement Learning Recurrent neural network · Long short‑term memory (LSTM) · Gated recurrent unit (GRU) · Translation · Beam search and width · BLEU score
- Unit 5 · Short notes Advanced Topics and Applications Support Vector Machines · Bayesian learning · Applications in computer vision · Speech processing · Natural language processing · Case Study: ImageNet Competition
More for CS-601
Other 6th semester CSE subjects
- CS-602 Computer Networks
- CS-603 Compiler Design
- CS-604 Rural Technology & Community Development
- CS-605 Data Analytics Lab
- CS-606 Skill Development Lab