Subject Details
Dept     : CS-AIDS
Sem      : 5
Regul    : 2021
Faculty : Dr.P.Sedhupathy
phone  : 7395895468
E-mail  : sedhupathy.p.aids@drsnsrcas.ac.in
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Syllabus

UNIT
1
INTRODUCTION TO MACHINE LEARNING

Introduction- What Is Human Learning?- Types Of Human Learning. What Is Machine Learning?- Types Of Machine Learning: Supervised Learning- Unsupervised Learning- Reinforcement Learning- Comparison – Supervised, Unsupervised, And Reinforcement Learning- Applications Of Machine Learning: Banking And Finance- Insurance- Healthcare.

UNIT
2
PREPARING TO MODEL

Machine Learning Activities- Basic Types Of Data In Machine Learning- Exploring Structure Of Data: Exploring Numerical Data- Plotting And Exploring Numerical Data- Exploring Categorical Data- Exploring Relationship Between Variables. Data Quality And Remediation: Data Quality- Data Remediation. Data Pre-Processing: Dimensionality Reduction- Feature Subset Selection.

UNIT
3
MODELLING AND EVALUATION

Selecting A Model: Predictive Models- Descriptive Models. Training A Model: Holdout Method- K-Fold Cross-Validation Method- Bootstrap Sampling- Lazy Vs. Eager Learner. Model Representation And Interpretability: Underfitting- Overfitting- Bias – Variance Trade-Off. Evaluating Performance Of A Model: Supervised Learning – Classification- Supervised Learning – Regression- Unsupervised Learning - Clustering -Improving Performance Of A Model.

UNIT
4
BASICS OF FEATURE ENGINEERING

What Is A Feature?- What Is Feature Engineering?- Feature Transformation: Feature Construction- Feature Extraction. Feature Subset Selection: Feature Construction- Feature Extraction- Feature Subset Selection: Issues In High-Dimensional Data- Key Drivers Of Feature Selection – Feature Relevance And Redundancy- Measures Of Feature Relevance And Redundancy- Overall Feature Selection Process.

UNIT
5
OTHER TYPES OF LEARNING

Representation Learning: Supervised Neural Networks And Multilayer Perceptron- Independent Component Analysis (Unsupervised)- Autoencoders- Various Forms Of Clustering- Active Learning: Heuristics For Active Learning- Active Learning Query Strategies- Instance-Based Learning (Memory-Based Learning): Radial Basis Function- Pros And Cons Of Instance-Based Learning Method. Association Rule Learning Algorithm: Apriori Algorithm- Eclat Algorithm- Ensemble Learning Algorithm: Bootstrap Aggregation (Bagging)- Boosting- Gradient Boosting Machines (Gbm).

Reference Book:

1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron. 2. Machine Learning for Absolute Beginners by Oliver 3. Theobald Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD by Jeremy Howard and Sylvain Gugger 4. Bayesian Reasoning and Machine Learning by David Barber.

Text Book:

Saikat Dutt,Subramanian Chandramouli,Amit Kumar Das, “Machine Learning:Published by Pearson India Education Services Pvt. Ltd, CIN:U72200TN2005PTC057128.

 

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