23AM01-MACHINE LEARNING

PREREQUISITE : Probability and Statistics, Data Warehousing and Data Mining 

COURSE OUTCOMES (COs): At the end of the course, student will be able to

 

CO1 : Understand development steps of model building and evaluation approaches. (Understand- L2)

CO2 : Apply Nearest Neighbor-based models to solve real-time regression and classification problems . (Apply -L3)

CO3 : Make use of supervised learning algorithms to solve classification problems. (Apply- L3) 

CO4 : Apply linear discriminants and perceptron classifiers to classify datasets. (Apply- L3)

CO5 : Apply various clustering techniques to solve complex problems. (Apply- L3)

UNIT-I:    Unit-1 PPT Link  Unit-1 Pdf 

Introduction to Machine Learning: Evolution of Machine Learning, Paradigms for ML, Learning by Rote, Learning by Induction, Reinforcement Learning, Types of Data, Matching, Stages in Machine Learning, Data Acquisition, Feature Engineering, Data Representation, Model Selection, Model Learning, Model Evaluation, Model Prediction, Search and Learning, Data Sets. 

UNIT-II:  Unit-2 PPT Link , Unit-2 Pdf Link, Proximity distance measures link

Nearest Neighbor-Based Models: Introduction to Proximity Measures, Distance Measures, Non-Metric Similarity Functions, Proximity Between Binary Patterns, Different Classification Algorithms Based on the Distance Measures ,K-Nearest Neighbor Classifier, Radius Distance Nearest Neighbor Algorithm, KNN Regression, Performance of Classifiers, Performance of Regression Algorithms. 

UNIT-III: Unit-3 PPT Link, Unit-3 Pdf Link

Models Based on Decision Trees: Decision Trees for Classification, Impurity Measures, Properties, Regression Based on Decision Trees, Bias–Variance Trade-off, Random Forests for Classification and Regression. The Bayes Classifier: Introduction to the Bayes Classifier, Bayes’ Rule and Inference, The Bayes Classifier and its Optimality, Multi-Class Classification, Class Conditional Independence and Naive Bayes Classifier (NBC) 

UNIT-IV: 

Linear Discriminants for Machine Learning: Introduction to Linear Discriminants, Linear Discriminants for Classification, Perceptron Classifier, Perceptron Learning Algorithm, Support Vector Machines, Linearly Non-Separable Case, Non-linear SVM, Kernel Trick, Logistic Regression, Linear Regression, Multi-Layer Perceptrons (MLPs), Backpropagation for Training an MLP. 

UNIT-V: 

Clustering : Introduction to Clustering, Partitioning of Data, Matrix Factorization, Clustering of Patterns, Divisive Clustering, Agglomerative Clustering, Partitional Clustering, K-Means Clustering, Soft Partitioning, Soft Clustering, Fuzzy C-Means Clustering, Rough Clustering, Rough K-Means Clustering Algorithm, Expectation Maximization-Based Clustering, Spectral Clustering. Text Books: 1. “Machine Learning Theory and Practice”, M N Murthy, V S Ananthanarayana, Universities Press (India), 2024

TEXTBOOKS: 

 T1 “Machine Learning Theory and Practice”, M N Murthy, V S Ananthanarayana, Universities Press (India), 2024. 

 REFERENCE BOOKS: 

 R1  “Machine Learning”, Tom M. Mitchell, McGraw-Hill Publication, 2017.

 R2   Peter Harington, “Machine Learning in Action”, Cengage, 1st edition, 2012 

 R3   Peter Flach, “Machine Learning: The art and science of algorithms that make sense of data”, Cambridge university press,2012.

 23AM51-MACHINE LEARNING LAB

Course Objectives: 

• To learn about computing central tendency measures and Data preprocessing techniques
 • To learn about classification and regression algorithms 
• To apply different clustering algorithms for a problem.

CO1: Apply the appropriate pre-processing techniques on dataset.(Apply–L3)
CO2: Implement supervised Machine Learning algorithms.(Apply–L3)
CO3: Implement advanced Machine Learning algorithms.(Apply–L3)
CO4: Improve individual/teamwork skills, communication & report writing skills with ethical values.

TEXTBOOKS:
T1“MachineLearningTheoryandPractice”,MNMurthy,VS Ananthanarayana,UniversitiesPress(India), 2024.
REFERENCEBOOKS:
R1“Machine Learning”,Tom M.Mitchell,McGraw-HillPublication,2017
R2“MachineLearning inAction”,PeterHarrington,DreamTech.
R3“IntroductiontoDataMining”, Pang-NingTan,MichelStenbach, VipinKumar,7^thEdition,2019.

Software Required: Python/R/Weka Lab should cover the concepts studied in the course work, sample list of Experiments:

 1. Compute Central Tendency Measures: Mean, Median, Mode Measure of Dispersion: Variance, Standard Deviation Cycle-1 Program. 

2.  Apply the following Pre-processing techniques for a given dataset. a. Attribute selection b. Handling Missing Values c. Discretization d. Elimination of Outliers.Cycle-2 Program1.py, Cycle -2 Program .ipynb file, Iris Dataset, Kidney-stone-Dataset.csv, Iris-Flower Dataset, Train_loan-Dataset, Cycle-2.1_ipynb, Cycle-2.2_ipynb, Cycle-2-Word, Cycle-2.1_word

3.  Apply KNN algorithm for classification and regression.

Cycle-3 Program link , Cycle-3 Program .py file

4. Demonstrate decision tree algorithm for a classification problem and perform parameter tuning for better results  Cycle -4 & 5 Programs Link, Play-Tennis, Housing Data, Heart Disease Prediction, Iris, Program link2
5. Demonstrate decision tree algorithm for a regression problem 
6. Apply Random Forest algorithm for classification and regression 
7. Demonstrate Naïve Bayes Classification algorithm. 
8. Apply Support Vector algorithm for classification
 9. Demonstrate simple linear regression algorithm for a regression problem 
10. Apply Logistic regression algorithm for a classification problem 
11. Demonstrate Multi-layer Perceptron algorithm for a classification problem 
12.Implement the K-means algorithm and apply it to the data you selected. Evaluate performance by measuring the sum of the Euclidean distance of each example from its class center. Test the performance of the algorithm as a function of the parameters K. 
13. Demonstrate the use of Fuzzy C-Means Clustering 
14. Demonstrate the use of Expectation Maximization based clustering algorithm