Machine Learning and Deep Learning Training Course

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

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

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

Artificial Neural Network

Deep Learning

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