Machine Learning
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Machine Learning Syllabus — Unit-wise Content
The official GTU curriculum for Machine Learning (subject code 3164803) as prescribed for CS-Cyber · Semester 6 divides the course into 6 units, covering 39 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 (3164803)Unit 1 INTRODUCTION TO MACHINE LEARNING
- Overview of Human Learning and Machine Learning
- Machine Learning basics
- Applications of ML, Data Mining Vs Machine Learning vs
- Big Data Analytics
- Types of Machine Learning: Supervised learning
- Unsupervised learning
- Reinforcement learning
- Applications of Machine Learning
Unit 2 FEATURE SELECTION AND DIMENSION REDUCTION
- Feature selection: Subset Selection
- Decision tree - Entropy reduction and information gain
- best first
- Greedy forward & backward
- Dimension Reduction : Principal Components Analysis
- Linear Discriminant Analysis
Unit 3 SUPERVISED MACHINE LEARNING
- Regression: Simple linear regression
- Multiple linear regression
- Polynomial regression
- evaluating regression fit
- Classification: Logistic Regression
- Decision tree
- Random forest
- Naive Bayes
- Support vector machine
Unit 4 UNSUPERVISED MACHINE LEARNING
- Grouping unlabeled items using k-means clustering
- Anomaly detection
- Hierarchical Clustering
Unit 5 DEVELOPMENT AND EVALUATION OF ML MODELS
- Steps in ML modeling
- Data Collection
- Data pre-processing
- Model Selection
- Cross validation
- Model evaluation: confusion matrix
- True positive
- false positive etc.
- Hyper parameter Tuning
- Grid Search
- Bias/variance tradeoff
Unit 6 ENSEMBLE TECHNIQUES
- Combining multiple learners: Model combination schemes
- Ensemble Learning - bagging
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