## Duration

21 hours (usually 3 days including breaks)

## Requirements

Basic knowledge of statistical concepts is desirable.

## Overview

This course covers AI (emphasizing Machine Learning and Deep Learning)

## Course Outline

**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