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Subject Syllabus

CY-701 - Machine Learning

CSCY · Semester 7

Course Modules

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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 and unsupervised learning.

Linearity vs non linearity, activation functions like sigmoid ReLU, etc., weights and bias, loss function, gradient descent, multilayer network, back propagation, weight initialization, training, testing, unstable gradient problem, auto encoders, batch normalization, dropout L1 and L2 regularization, momentum, tuning hyper parameters.

Convolutional neural network flattening subsampling padding stride convolution layer pooling layer loss layer dance layer 1x1 convolution inception network input channels transfer learning one shot learning dimension reductions implementation of CNN like tensor flow keras etc.

Recurrent neural network Long short-term memory, gated recurrent unit, translation, beam search and width Bleu score, attention model Reinforcement Learning RL-framework MDP, Bellman equations Value Iteration and Policy Iteration, , Actor-critic model Q-learning SARSA.

Support Vector Machines Bayesian learning, application of machine learning in computer vision, speech processing, natural language processing etc Case Study: ImageNet Competition.Recommended Books with Full Specification: