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Dr. Rambabu Pemula

Machine Learning2: UNIT-5(A) NOTES: Analytical Learning NOTES

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A CONCEPT LEARNING TASK
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and Reinforcement Learning
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Callback
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checkers game
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clone
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CNN
Computational Complexity
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CONCEPT LEARNING
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Concept Learning and the General to Specific Ordering
Concept Learning as Search
Concept Learning Task
conflict
Confusion Matrix
consistent
constraints
Convolution
Convolution Layer
Convolution Neural Network Layer
Correlation Analysis
Correlation Matrix
Cross-Entropy Loss Function
Crossover
CSV
CSV file
data augmentation
Data Preprocessing
Data Wrangling
DBSCAN
decision boundary
Decision function and Predictions
Decision Tree
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Decision Tree Program
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Decision Trees
deep autoencoders
Deep Learning
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Defining Success
definition of machine learning
Degree for Polynomial
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dense layer
DESIGNING A LEARNING SYSTEM
Determining Optimal Gamma
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Dictionary
Difficulties in Convergence
Dimensionality Reduction
Diving into Transfer Learning
DL Performance
driverless car
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DVCS
EM Algorithm
Enjoy Sport
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Ensemble Learning MCQs
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Entropy
Equivalence of DFA & NFA
Essential Elements
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Euclidean distance
Examples of Machine Learning Applications
Excel
experience
Explanation-Based Learning of Search Control Knowledge
F1 score
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False Negative Rate
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FC
Feature Count
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Find-S
FIND-S Algorithm
Find-S: Algorithm
finding similarities
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Flatten
forking
Forward
Forward Pass
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general hypothesis
general-to-specific ordering of hypothesis
Generalizing from Examples
Genetic Algorithm MCQs
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Genetic Operators
Genetic Programming
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Gini Impurity or Entropy?
Gini Index
GIT
GITHUB
gradient
Gradient Boosting
GRU
ham
Handling Branches
handwriting recognition learning problem.
Handwritten digit
Handwritten Digit classification
Hard Margin
Hard Voting
Heart Disease Dataset
Hierarchical Clustering
Hinge Loss
How does clustering work
How Machine Learning Works? Machine Learning
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hypothesis space
ID3
ID3 Algorithm
ILP
image
Importance of Defining Success
Impurity
Indent level 4
Information Gain
Instance
instance based learning
instance space
INSTANCE-BASED LEARNING
Instance-Based Versus Model-Based Learning
instances
Insufficient Quantity of Training Data
Internal Factor
Intro to Hypothesis Testing
Introduction
Introduction to Supervised
Iris
IRIS dataset
Irrelevant Features
Issues In Decision Tree Learning
JNTUA
JNTUH
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KPI for Consumer Service
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Learning Rule Sets: Summary
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Learning Sets of Rules
Learning With Perfect Domain Theories: PROLOG-EBG
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list-then-elimination algorithm
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machine learning. spam detection
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MCQs
Measuring Accuracy Using Cross-Validation
merge
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Missing Values
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MNIST
MNIST Dataset
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Models
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more general than hypothesis
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Motivation
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Multilayer Networks
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Naive Bayes Algorithm
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Navie Bayes
Need for NN
Negative Training Example
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Neural Network MCQs
Neural Network Representation
Neural Networks
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NFA
Nodes
non-parametric Locally Weighted Regression Algorithm
Non-Representative
Nondeterministic Rewards and Actions
Nonlinear SVM Classification
Nonrepresentative Training Data
normalizing features
North Star for Companies
North Star for Instagram
Not 5
online learning
optimizer
OvA
Overfitting
OvO
Padding
Parallelizing Genetic Algorithms
Pasting
PCA
PDA
penalty
Perceptron
Perceptron’s
performance
Performance Measures
Performance Metrics
PERSPECTIVES AND ISSUES IN MACHINE LEARNING
Plotting customers with their segments
Polynomial Kernel
Pooling
Pooling Layer
Poor-
Poor-Quality Data
Positive Training Example
Precision
Precision and Recall
Precision/recall trade-off
Prediction
Preprocessing
Print
Process
Product Metrics
Product Strategy & Business Acumen
Programs
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pull
push
Python
Q-Learning
Q–learning
Quadratic Programming
Random Forest
Random Forests
RandomForestClassifier
RBF
Read
Reader
Recall
Recommendation system
Regular Expression
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Regularization Hyperparameters
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reinforcement
Reinforcement Learning
Relationship to Dynamic Programming
ReLU
Remarks on Lazy and Eager Learning
repository
RNN
RNNLayer
RNNNode
ROC
rotation
Row
Scale
scaling
semi supervised
semi supervised learning
Sequential Covering Algorithms
SGD Classifier
SGDClassifier Model
Short Answer Questions
Sigmoid
simple learning process
sklearn
soft margin
Soft Margin Classification
Soft Voting
SoftMax
spam
spam classification
spam filter
Sparse Autoencoders
specific hypothesis
specific training examples.
stacking
Stride
Strings
supervised
supervised learning
Supervised/Unsupervised Learning
Support Vector Machine
Support Vector Machines
SVC
SVM
SVM algorithm
SVM Precision
SVM Regression
Syllabus
T-test
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target function
target concept
task
Temporal Difference Learning
TensorFlow
Testing
the Back-Propagation Algorithm
The Basic Decision Tree Learning Algorithm
The CART Training Algorithm
The Curse of Dimensionality
The Dual problem
The learning task
The Machine Learning Landscape
The Softmax Function
The Vanishing and Exploding Gradient Problems
Tolerance
traditional programming
Trainer and Optimizer
training
Training a Binary Classifier
Training and Visualizing a Decision Tree
training examples
Training Objective
Transfer Learning
True Negative Rate
True Positive Rate
Turing Machines
Types
Types of Machine Learning Systems
Under Fitting
Under the Hood
Underfitting
Unsupervised
Unsupervised and Reinforcement Learning
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
White Box
Why NN
why use machine learning
Why Use Machine Learning?
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Dr. RAMBABU PEMULA

Machine Learning: India

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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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Machine Learning2: UNIT-5(A) NOTES: Analytical Learning NOTES

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