Predictive Analytics
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Predictive Analytics Syllabus — Unit-wise Content
The official GTU curriculum for Predictive Analytics (subject code 3164205) as prescribed for AI & DS · Semester 6 divides the course into 5 units, covering 40 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (3164205)Unit 1 Regression
- Overview of supervised and unsupervised machine learning
- Predictive model
- Descriptive model
- Simple linear regression and least squares
- Multiple linear regression
- Regression model evaluation
- Ridge regression
- Lasso regression Subject Code : 3164205 Subject Name : Predictive Analytics
Unit 2 Classification
- Logistic regression
- Naïve Bayes classifier
- SVM for classification
- SVM for regression
- K-nearest neighbor classifiers
Unit 3 Evaluating ML models
- Variance and model complexity
- Bias-variance tradeoff
- Cross validation: Holdout method
- K-fold cross validation
Unit 4 Additive Models, Trees, and Boosting
- Generalized additive models
- Regression and classification trees
- Boosting methods-exponential loss and AdaBoost
- Numerical Optimization via gradient boosting
Unit 5 Unsupervised Learning and Random forests
- Association rules
- Cluster analysis
- Principal Components
- Random forests and analysis
- Total Hours : 36 100 % Textbook : 1
- An Introduction to Statistical Learning with Applications in R, Gareth James
- Daniela Witten
- Trevor Hastie, and Robert Tibshirani
- The Elements of Statistical Learning-Data Mining
- Inference, and Prediction
- Trevor Hastie
- Robert Tibshirani
- Jerome Friedman
- Second Edition
- Springer Verlag
- Machine Learning
- Saikat Dull, S
- Chjandramouli, Das, Pearson 4
- Bishop – Pattern Recognition and Machine Learning
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