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Machine Learning-I BAI602

Machine Learning-I BAI602

Download vtu notes model paper previous year paper of 6th semester Machine Learning-I BAI602 2022 scheme..

Machine Learning-I BAI602

Course Code: BAI602

Credits: 04

CIE Marks: 50

SEE Marks: 50

Total Marks: 100

Exam Hours: 03

Total Hours of Pedagogy: 50H

Teaching Hours/Weeks: [L:T:P:S] 4:0:0:0

Introduction: Need for Machine Learning, Machine Learning Explained, Machine Learning in Relation to other Fields, Types of Machine Learning, Challenges of Machine Learning, Machine Learning Process, Machine Learning Applications.

Understanding Data – 1: Introduction, Big Data Analysis Framework, Descriptive Statistics, Univariate Data Analysis and Visualization.

Understanding Data – 2: Bivariate Data and Multivariate Data, Multivariate Statistics, Essential Mathematics for Multivariate Data, Feature Engineering and Dimensionality Reduction Techniques.

Testing Machine Learning Algorithms: Overfitting , Training, Testing, and Validation Sets ,The Confusion Matrix , Accuracy Metrics , The Receiver Operator Characteristic (ROC) Curve , Unbalanced Datasets , Measurement Precision.

Similarity-based Learning: Nearest-Neighbor Learning, Weighted K-Nearest-Neighbor Algorithm, Nearest Centroid Classifier, Locally Weighted Regression (LWR).

Regression Analysis: Introduction to Regression, Introduction to Linear Regression, Multiple Linear Regression, Polynomial Regression, Logistic Regression.

Decision Tree Learning: Introduction to Decision Tree Learning Model, Decision Tree Induction Algorithms. Validating and pruning of Decision trees.

Bayesian Learning: Introduction to Probability-based Learning, Fundamentals of Bayes Theorem, Classification Using Bayes Model, Naïve Bayes Algorithm for Continuous Attributes.

Artificial Neural Networks: Introduction, Biological Neurons, Artificial Neurons, Perceptron and Learning Theory, Types of Artificial Neural Networks, Popular Applications of Artificial Neural Networks, Advantages and Disadvantages of ANN, Challenges of ANN.

Clustering Algorithms: Introduction to Clustering Approaches, Proximity Measures, Hierarchical Clustering Algorithms, Partitional Clustering Algorithm, Density-based Methods, Grid-based Approach.

2022 SCHEME QUESTION PAPER

Regular Paper

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GURUSIDDA
GURUSIDDA
24-06-2025 8:11 PM

model qp or importent questions bro

rachanaa
rachanaa
26-06-2025 4:30 PM

can we refer cs paper for our branch

Sanyam
Sanyam
26-06-2025 5:28 PM

bro please upload imp questions for ML

Anonymous
Anonymous
26-06-2025 7:24 PM

Important questions please

Sindhu
Sindhu
26-06-2025 8:46 PM

Pls upload Mqp soon

Ash
Ash
27-06-2025 11:02 AM

Please check for a MQP because CS has different portions and AIML has different portions. We cannot refer CS MQP at all. We literally are left with no option but to read whole Textbook

Ranganath
Ranganath
27-06-2025 11:41 AM

sir please send important questions

Sushma
Sushma
28-06-2025 3:43 PM

please provide materials

Sushma
Sushma
29-06-2025 6:46 AM

Please provide notes

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