Week 1: Introduction
Introduction to ML, Performance Measures, Bias-Variance Trade off, Linear Regression.
Week 2: Bayes Decision Theory
Bayes Decision Theory, Normal Density and Discriminant Function, Bayes Decision Theory - Binary
Features, Bayesian Belief Network
Week 3: Parametric and Non- Parametric Density Estimation
Parametric and Non- Parametric Density Estimation – ML and Bayesian Estimation, Parzen Window
and KNN
Week 4:Perceptron Criteria and Discriminative Models
Perceptron Criteria, Discriminative models, Support Vector Machines (SVM)
Week 5: Logistic Regression, Decision Trees and Hidden Markov Model
Logistic Regression, Decision trees, Hidden Markov Model (HMM)
Week 6: Ensemble methods
Ensemble methods: Ensemble strategies, boosting and bagging, Random Forest
Week 7: Dimensionality Problem
Dimensionality Problem, Principal Component Analysis (PCA), Linear Discriminant Analysis
(LDA)
Week 8: Mixture Model and Clustering
Concept of mixture model, Gaussian mixture model, Expectation Maximization Algorithm, K-
means clustering.
Week 9: Clustering
Fuzzy K-means clustering, Hierarchical Agglomerative Clustering, Mean-shift clustering.
Week 10: Neural Network
Neural network: Perceptron, multilayer network, backpropagation, RBF Neural Network,
Applications
Week 11: Introduction to Deep Neural Networks
Introduction to Deep Learning, Convolutional Neural Networks (CNN),
Vanishing and Exploding Gradients in Deep Neural Networks, LeNet - 5, AlexNet, VGGNet, GoogleNet, and ResNet.
Week 12: Recent Trends in Deep Learning
Generative Adversarial Networks (GAN), Auto Encoders and Relation to PCA, Recurrent Neural Networks, U-Net, Applications and Case studies.
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