AI and Machine Learning with R

Course Objective

This course introduces PGDM students to artificial intelligence and machine learning using the R programming language. It blends statistical thinking with practical applications of ML techniques for business problem-solving, using R libraries and tools. Emphasis is placed on data preparation, model building, evaluation, and communication of AI-driven insights.

Learning Outcomes

  • Perform data wrangling, visualization, and exploratory analysis using R.
  • Build supervised and unsupervised machine learning models using R packages.
  • Apply AI techniques like neural networks, decision trees, and ensemble models.
  • Integrate ML solutions into business domains such as marketing, finance, and operations.
  • Communicate model outcomes and business insights effectively.

AI and Machine Learning with R Syllabus T30

Session No. Topics Tool/Reading/Activity Skill Focus
1 Introduction to AI & ML in Business Context Course Slides Strategic Thinking
2 Getting Started with R & RStudio R Setup & Console Lab Digital Fluency
3 Data Wrangling with dplyr & tidyr Tidyverse Lab Data Cleaning
4 Data Visualization using ggplot2 Charts & Graphs Workshop Visual Communication
5 Introduction to Supervised Learning caret Overview Predictive Thinking
6 Linear and Logistic Regression Modeling in caret Forecasting
7 Model Evaluation: Accuracy, AUC, Confusion Matrix Metrics Lab Model Assessment
8 Decision Trees & Random Forest rpart & randomForest Classification Strategy
9 KNN, Naïve Bayes & Support Vector Machines ML Package Demos Comparative Analysis
10 Unsupervised Learning: Clustering Techniques kmeans & hclust Segmentation
11 Dimensionality Reduction with PCA FactoMineR Feature Engineering
12 Ensemble Methods: Bagging, Boosting xgboost & caret Accuracy Enhancement
13 Neural Networks in R nnet & keras Deep Learning Basics
14 Time Series Forecasting forecast Package Temporal Analysis
15 Text Mining & Sentiment Analysis tm & tidytext NLP
16 AI Use Cases in Marketing, HR, and Finance Applied Labs Domain Relevance
17 Model Deployment & R Markdown Reporting RStudio Projects Presentation Skills
18 Ethics & Explainability in AI Class Discussion Responsible AI
19 Capstone Project: AI Model for Business Insight Team Implementation Solution Design
20 Project Presentations & Peer Review Final Showcase Strategic Communication

Textbook & Resources

Primary Tools & Libraries:
  • R, RStudio, Tidyverse, caret, ggplot2, randomForest, xgboost, keras, nnet
Reference Books:
  • Machine Learning with R by Brett Lantz
  • Hands-On Machine Learning with R by Brad Boehmke and Brandon Greenwell