Machine Learning and Deep Learning Training Course

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