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Dr. Rambabu Pemula

SOFTWARE ENGINEERING UNIT - 1 (B) PROCESS MODELS PPTS

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Machine Learning 1: UNIT 3 : Support Vector Machines MCQs

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Dr. RAMBABU PEMULA

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Concept Learning as Search
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conflict
Confusion Matrix
consistent
constraints
Convolution
Convolution Layer
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Correlation Analysis
Correlation Matrix
Cross-Entropy Loss Function
Crossover
CSV
CSV file
data augmentation
Data Preprocessing
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DBSCAN
decision boundary
Decision function and Predictions
Decision Tree
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Decision Trees
deep autoencoders
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Defining Success
definition of machine learning
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Denoising autoencoders
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DESIGNING A LEARNING SYSTEM
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Dictionary
Difficulties in Convergence
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Diving into Transfer Learning
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driverless car
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Enjoy Sport
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Explanation-Based Learning of Search Control Knowledge
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Find-S: Algorithm
finding similarities
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Flatten
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GITHUB
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GRU
ham
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handwriting recognition learning problem.
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How does clustering work
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hypothesis space
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ID3 Algorithm
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normalizing features
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Perceptron’s
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Performance Metrics
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Plotting customers with their segments
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Positive Training Example
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rotation
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Scale
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Short Answer Questions
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simple learning process
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the Back-Propagation Algorithm
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traditional programming
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unsupervised learning
upstream
Vanilla RNNs
variational autoencoders
Version Spaces and the Candidate Elimination Algorithm
VERSION SPACES AND THE CANDIDATE-ELIMINATION ALGORITHM
voting
Voting Classifiers
Weight Impact
Weight Initialization
WELL-POSED LEARNING PROBLEMS
What Is Machine Learning?
What Is Machine Learning? Why Use Machine Learning? Types of Machine Learning Systems Supervised/Unsupervised Learning Batch and Online Learning Instance-Based Versus Model-Based Learning
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why use machine learning
Why Use Machine Learning?
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Dr. RAMBABU PEMULA

Machine Learning: India

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About Formal Languages and Automata Theory

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Deep Learning
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Machine Learning - Deep Learning

Dr. Rambabu Pemula

MACHINE LEARNING

Machine Learning Syllabus
MACHINE LEARNING UNIT - I PPTs Introduction, Concept Learning and the General to Specific Ordering
MACHINE LEARNING UNIT-1 Note : INTRODUCTION, CONCEPT LEARNING
MACHINE LEARNING UNIT – 2 (A) Notes: Decision Tree Learning
MACHINE LEARNING Unit-2 (A) PPTs: Decision Tree Learning
MACHINE LEARNING UNIT-2(B) PPTs Artificial Neural Networks
MACHINE LEARNING Unit-2 (B)Notes: Artificial Neural Networks
MACHINE LEARNING UNIT-3 (A) Notes: Bayesian Learning
MACHINE LEARNING UNIT-3 (A) PPTs: Bayesian Learning
MACHINE LEARNING UNIT-3 (B) Notes: Instance-Based Learning
MACHINE LEARNING UNIT-3 (B) PPTs: Instance-Based Learning
MACHINE LEARNING UNIT-4(A) Notes: Genetic Algorithms
MACHINE LEARNING UNIT-4 (A) PPTs: Genetic Algorithms
MACHINE LEARNING UNIT- 4 (B) NOTES: LEARNING SETS OF RULES
MACHINE LEARNING UNIT-4 (B) PPT's : Learning Sets of Rules
MACHINE LEARNING UNIT-5 (A) NOTES: ANALYTICAL LEARNING
MACHINE LEARNING UNIT-5(A) PPT's: Analytical Learning
MACHINE LEARNING UNIT- 5 (B) NOTES: REINFORCEMENT LEARNING
MACHINE LEARNING UNIT-5 (B) PPTs: Reinforcement Learning
Machine Learning MCQs-1
Machine Learning MCQs-2 (Performance Metrics, Linear Regression, Naïve Bayes Classifier )
UNIT: 3 Bayes Theorem, Naive Bayes Algorithm, KNN Algorithm MCQs
Machine Learning MCQs-3 (Logistic Regression, KNN, SVM, Decision Tree)
UNIT- 4: Genetic Algorithm MCQs
Machine Learning MCQs - 4 (Clustering, Dimensionality Reduction)
UNIT - 5: Reinforcement Learning MCQs
Machine Learning MCQs - 5 (Ensemble Models)

MACHINE LEARNING PROGRAMS

WEEK 1 : a) Write a program using scikit-learn to implement K-means Clustering
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WEEK 1 : b) Program to calculate the entropy and the information gain
WEEK 1 : c) Program to implement perceptron.
WEEK 2: • Implement and demonstrate the FIND-S algorithm for finding the most specific hypothesis based on a given set of training data samples. Read the training data from a .CSV file.
WEEK 3: • For a given set of training data examples stored in a .CSV file, implement and demonstrate the Candidate-Elimination algorithm to output a description of the set of all hypotheses consistent with the training examples.
WEEK 4: • Write a program to demonstrate the working of the decision tree based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample.
WEEK 5: • Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets.
WEEK 6- Write a program to construct a Bayesian network considering medical data. Use this model to demonstrate the diagnosis of heart patients using a standard Heart Disease Data Set.
WEEK 7: • Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data set for clustering using k-Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering.
WEEK 8: Write a program to implement k-Nearest Neighbors algorithm to classify the iris data set. Print both correct and wrong predictions.
WEEK 9 - Write a program to implement SVM algorithm to classify the iris data set. Print both correct and wrong predictions.
WEEK 10: Implement the non-parametric Locally Weighted Regression algorithm in order to fit data points. Select appropriate data set for your experiment and draw graphs.
Data Preprocessing in Machine Learning
Data Preprocessing in Machine learning (Handling Missing values )
Linear Regression - ML Program - Weight Prediction
Naïve Bayes Classifier - ML Program
Naïve Bayes Classifier
Naive Bayes Classifier - Example -classify- play tennis - forecast
KNN Machine Learning Program
Support Vector Machine (SVM) - ML Program
Decision Tree Classifier on Iris Dataset
Classification of Iris flowers using Random Forest
DBSCAN

ARTIFICIAL INTELLIGENCE

ARTIFICIAL INTELLIGENCE SYLLABUS
ARTIFICIAL INTELLIGENCE UNIT-1(A) Introduction to Artificial Intelligence PPTS
ARTIFICIAL INTELLIGENCE UNIT-1(B) Problem Solving PPTS
ARTIFICIAL INTELLGENCE UNIT-2 (A) Problem Reduction and Game Playing PPTS
ARTIFICIAL INTELLIGENCE UNIT-2 B Logic Concepts PPTs
ARTIFICIAL INTELLIGENCE UNIT-3(A) Knowledge Representation ppts
ARTIFICIAL INTELLIGENCE UNIT-3(B) Advance Knowledge Representation Techniques ppts
ARTIFICIAL INTELLIGENCE UNIT-4 Expert Systems PPTs
ARTIFICIAL INTELLIGENCE UNIT 5 (A) Uncertainty measure PPTs
ARTIFICIAL INTELLIGENCE UNIT 5 (B) Fuzzy sets and fuzzy logic PPTs

DATA WRANGLING

Data Wrangling Syllabus
DATA WRANGLING UNIT-1 (A) Introduction to Data Wrangling PPTs
DATA WRANGLING UNIT-1(C) Data Meant to Be Read by Machines PPTs
DATA WRANGLING UNIT-2 (A) Working with Excel Files PPTs
DATA WRANGLING UNIT-2 (B) Working with PDFs PPTs
DATA WRANGLING UNIT-2 (C) Acquiring and Storing Data PPTs
DATA WRANGLING UNIT-3 (A) Data Cleanup PPTS
DATA WRANGLING UNIT-3 (B) Data Cleanup- Standardizing and Scripting PPTs
DATA WRANGLING UNIT-4(A) Data Exploration and Analysis PPTs
DATA WRANGLING UNIT-4(B) Presenting Your Data PPTs
DATA WRANGLING UNIT-5 (A) WEB SCRAPING PPT
DATA WRANGLING UNIT-5(B) Advanced Web Scraping PPTs

SOFTWARE ENGINEERING

Software Engineering Syllabus
SOFTWARE ENGINEERING UNIT-1 (A ) SOFTWARE AND SOFTWARE ENGINEERING PPTS
SOFTWARE ENGINEERING UNIT - 1 (B) PROCESS MODELS PPTS
SOFTWARE ENGINEERING UNIT-2 (A) Requirements Analysis and Specification PPTS
SOFTWARE ENGINEERING UNIT-2(B) Software Design PPT
SOFTWARE ENGINEERING UNIT-III(A) FUNCTION-ORIENTED SOFTWARE DESIGN PPTs
SOFTWARE ENGINEERING UNIT-III (B) USER INTERFACE DESIGN PPTs
SOFTWARE ENGINEERING UNIT-IV CODING & TESTING PPTs
SOFTWARE ENGINEERING UNIT-V (A) Software Reliability and Quality Management PPTs
SOFTWARE ENGINEERING UNIT-V (B) Software Reliability and Quality Management PPTs
SOFTWARE ENGINEERING UNIT-VI (A) Software Maintenance & Software Reuse PPTs
SOFTWARE ENGINEERING UNIT-VI (B) Software Maintenance & Software Reuse PPTs

DATA WRANGLING PROGRAMS

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CSV File Reading
Read each row from a given csv file and print a list of strings
Read a given CSV file as a Dictionary.
JSON File Reading
Convert Python dictionary object (sort by key) to JSON data. Print the object members with indent level 4
XML File Reading
Excel File Reader

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SOFTWARE ENGINEERING UNIT - 1 (B) PROCESS MODELS PPTS

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