Machine Learning 3170724 Syllabus Download With Weightage

Machine Learning 3170724 Syllabus Download With Weightage

Machine Learning 3170724 is a term that refers to Computer Department covers this subject This year, this Subject is covered in the 7th Semester.   

Sr. No.
1 Introduction to Machine Learning:
Overview of Human Learning and Machine Learning, Types of Machine Lear
2 Preparing to Model:
Machine Learning activities, Types of data in Machine Learning, Structures of data, Data
quality and remediation, Data Pre-Processing: Dimensionality reduction, Feature subset
3 Modelling and Evaluation:
Selecting a Model: Predictive/Descriptive, Training a Model for supervised learning,
model representation and interpretability, Evaluating performance of a model, Improving
performance of a model
4 Basics of Feature Engineering:
Feature and Feature Engineering, Feature transformation: Construction and extraction,
Feature subset selection : Issues in high-dimensional data, key drivers, measure and overall
5 Overview of Probability:
Statistical tools in Machine Learning, Concepts of probability, Random variables, Discrete
distributions, Continuous distributions, Multiple random variables, Central limit theorem,
Sampling distributions, Hypothesis testing, Monte Carlo Approximation
6 Bayesian Concept Learning:
Impotence of Bayesian methods, Bayesian theorem, Bayes’ theorem and concept learning,
Bayesian Belief Network
7 Supervised Learning: Classification and Regression:
Supervised Learning, Classification Model, Learning steps, Classification algorithms,
Regression, Regression algorithms,
8 Unsupervised Learning:
Supervised vs. Unsupervised Learning, Applications, Clustering, Association rules
9 Neural Network:
Introduction to neural network, Biological and Artificial Neurons, Types of Activation
functions, Implementation of ANN, Architecture, Leaning process, Backpropogation, Deep


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