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• Machine learning
  Introduction to Machine Learning
• Applications of machine learning
  Supervised Versus Unsupervised Learning
  Machine Learning Algorithms
  Regression
  Classification
  Clustering
  Recommender System
  Anomaly Detection
  Reinforcement Learning
  Regression
• Simple & Multiple Regression
  Least Square Method
  Estimating the Coefficients
  Assessing the Accuracy of the Coefficient Estimates
  Assessing the Accuracy of the Model
  Post Estimation Analysis
  Other Considerations in the Regression Models
  Qualitative Predictors
  Extensions of the Linear Models
  Potential Problems
  Bias-variance trade off [under-fitting/over-fitting] for regression models
  Resampling Methods
• Cross-Validation
  The Validation Set Approach
  Leave-One-Out Cross-Validation
  k-Fold Cross-Validation
  Bias-Variance Trade-Off for k-Fold
  The Bootstrap
  Model Selection and Regularization
• Subset Selection [Best Subset Selection, Stepwise Selection, Choosing the Optimal Model]
  Shrinkage Methods/ Regularization [Ridge Regression, Lasso & Elastic Net]
  Selecting the Tuning Parameter
  Dimension Reduction Methods
  Principal Components Regression
  Partial Least Squares
  Classification
• Logistic Regression
• The Logistic Model cost function
• Estimating the Coefficients
• Making Predictions
• Odds Ratio
• Performance Evaluation Matrices
• [Sensitivity/Specificity/PPV/NPV, Precision, ROC curve etc.]
• Multiple Logistic Regression
• Logistic Regression for >2 Response Classes
• Regularized Logistic Regression
• Linear Discriminant Analysis
• Using Bayes’ Theorem for Classification
• Linear Discriminant Analysis for p=1
• Linear Discriminant Analysis for p >1
• Quadratic Discriminant Analysis
• K-Nearest Neighbors
• Classification with Non-linear Decision Boundaries
• Support Vector Machines
• Optimization Objective
• The Maximal Margin Classifier
• Kernels
• One-Versus-One Classification
• One-Versus-All Classification
• Comparison of Classification Methods
• Introduction to Deep Learning
  ANN Structure
• Biological neurons and artificial neurons
• Non-linear Hypothesis
• Model Representation
• Examples & Intuitions
• Transfer Function/ Activation Functions
• Typical classes of network architectures
• Feed forward ANN.
• Structures of Multi-layer feed forward networks
• Back propagation algorithm
• Back propagation - training and convergence
• Functional approximation with back propagation
• Practical and design issues of back propagation learning
• Deep Learning
• Artificial Intelligence & Deep Learning
• Softmax Regression
• Self-Taught Learning
• Deep Networks
• Demos and Applications
• Lab:
  Getting Started with R
• Introduction to R
• Basic Commands & Libraries
• Data Manipulation
• Importing & Exporting data
• Graphical and Numerical Summaries
• Writing functions
• Regression
• Simple & Multiple Linear Regression
• Interaction Terms
• Non-linear Transformations
• Dummy variable regression
• Cross-Validation and the Bootstrap
• Subset selection methods
• Penalization [Ridge, Lasso, Elastic Net]
• Classification
• Logistic Regression, LDA, QDA, and KNN,
• Resampling & Regularization
• Support Vector Machine
• Resampling & Regularization
• Note:
• For ML algorithms, case studies will be used to discuss their application, advantages & potential issues.
• Analysis of different data sets will be performed using R