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.
model qp or importent questions bro
can we refer cs paper for our branch
If syllabus is matching then you can refer…
bro please upload imp questions for ML
I didn’t get any important question from any faculty..
Important questions please
I didn’t get any important question from any faculty..
Pls upload Mqp soon
Model Paper not released for this subject…
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
sir please send important questions
please provide materials
Please provide notes