Machine Learning and Deep Learning

• Basic knowledge of statistical concepts is desirable

Audience

• Data scientists
• Machine learning engineers
• Software developers interested in AI
• Researchers working with data modeling
• Professionals looking to apply machine learning in business or industry

Machine Learning and Deep Learning are key areas of Artificial Intelligence (AI) that enable systems to learn patterns from data and make intelligent decisions.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to develop their understanding of machine learning algorithms, deep learning techniques, and AI-driven decision-making. The course provides hands-on experience with machine learning concepts, deep learning models, and practical implementations using R.

By the end of this training, participants will be able to:

• Understand the fundamentals of machine learning and deep learning.
• Apply various machine learning algorithms for regression, classification, clustering, and anomaly detection.
• Use deep learning architectures such as artificial neural networks (ANNs).
• Implement supervised and unsupervised learning models.
• Evaluate model performance and optimize hyperparameters.
• Use R for data analysis, visualization, and machine learning applications.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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 Regression Models
  • Qualitative Predictors
  • Extensions of 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
• 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

Deep Learning

Introduction to Deep Learning

Artificial Neural Networks (ANNs)

• Biological neurons and artificial neurons
• Non-linear Hypothesis
• Model Representation
• Examples & Intuitions
• Transfer Function/Activation Functions
• Typical Classes of Network Architectures
  • Feedforward ANN
  • Multi-layer Feedforward Networks
• Backpropagation Algorithm
• Backpropagation - Training and Convergence
• Functional Approximation with Backpropagation
• Practical and Design Issues of Backpropagation 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

Notes:

• For ML algorithms, case studies will be used to discuss their application, advantages, and potential issues.
• Analysis of different datasets will be performed using R.