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

Requirements

Basic knowledge of statistical concepts is desirable.

  21 Hours
 

Testimonials

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