Machine Learning (ML)
CS-601-
Unit 1Introduction to Machine Learning
Introduction to machine learning · Scope and limitations · Regression · Probability · Statistics and linear algebra for machine learning · Convex optimization · Data visualization · Hypothesis function and testing · Data distributions · Data preprocessing · Data augmentation · Normalizing data sets · Machine learning models · Supervised learning · Unsupervised learning
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Unit 2Deep Learning Foundations
Linearity vs non linearity · Activation functions (sigmoid, ReLU, etc.) · Weights and bias · Loss function · Gradient descent · Multilayer network · Backpropagation · Weight initialization · Training · Testing · Unstable gradient problem · Autoencoders · Batch normalization · Dropout · L1 and L2 regularization · Momentum · Tuning hyperparameters
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Unit 3Convolutional Neural Networks
Convolutional neural network · Flattening · Subsampling · Padding · Stride · Convolution layer · Pooling layer · Loss layer · 1x1 convolution · Inception network · Input channels · Transfer learning · One‑shot learning · Dimension reductions · Implementation of CNN using TensorFlow, Keras etc.
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Unit 4Recurrent Neural Networks and Reinforcement Learning
Recurrent neural network · Long short‑term memory (LSTM) · Gated recurrent unit (GRU) · Translation · Beam search and width · BLEU score · Attention model · Reinforcement Learning · RL framework · Markov Decision Process (MDP) · Bellman equations · Value iteration · Policy iteration · Actor‑critic model · Q‑learning · SARSA
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Unit 5Advanced Topics and Applications
Support Vector Machines · Bayesian learning · Applications in computer vision · Speech processing · Natural language processing · Case Study: ImageNet Competition