Machine Learning and Deep Learning Training Course

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