Lecture Notes || SNS Courseware

Subject Details

Dept : AIML

Sem : 4

Regul : R2023

Faculty : Nandhini N

phone : NIL

E-mail : nandhujee_01@rediffmail.com

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Lecture Notes

UNIT 1:

Machine Learning–Types of Machine Learning

Machine Learning process- preliminaries

Testing Machine Learning algorithms

Turning data into Probabilities and Statistics for Machine Learning -

Probability theory – Probability Distributions

Decision Theory

Turning data into Probabilities and Statistics for Machine Learning -

Bias, Variance and Tradeoff

1. Implement data crawlers Beautiful Soup, Ixml and scrapy 2. Implement data analyzing for a data set using SciPy and generate graph using NetworkX

3. Implement Resource Description Frame work by DBpedia 4. Create one- and two-dimensional random dataset and implement Series and Data Frames in python using slicing methods.

5. Implement the python code to compute the population proportions for the given problem

UNIT 2:

SVM and Hyperparameter tuning

Logistic Regression

Polynominal Regression

Linear Regression

Implementing SVM using scikit-learn

1. Implement Linear and Logistics regression using SciKit Learn [May use appropriate library]

Understanding cost function and gradient descent - Overfitting and Underfitting

2. Implement Stock Price Prediction based on Scikit Learn [May use appropriate library]

UNIT 3:

Entropy and The CART Training Algorithm

Decision Tree - Training and Visualizing a Decision Tree

Random Forests

1. Predict house prices based on several parameters available in the Housing and Urban Development of any dataset using least squares linear regression.

Ensemble

Boosting

UNIT 4:

KNearest Neighbours

Clustering- K-means

Hierarchical Clustering

Dimensionality Reduction

Principal Components Analysis,

Linear Discriminant Analysis

EM Algorithm- Mixtures of Gaussians

Principal Components Analysis,

2. Implement Market Segmentation using K-means Clustering.

3. Implement k-Nearest Neighbour algorithm to classify the provided data sets.

UNIT 5:

Introduction - Single State Case

Elements of Reinforcement Learning

Markov Decision Process

Q Learning Algorithm

Model Based Learning – Model Free Learning

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