IT IV-I FUNDAMENTALS OF MACHINE LEARNING

-

 

FUNDAMENTALS OF MACHINE LEARNING


Unit – I

Introduction andConcept Learning: Well-posed learning problems, designing a learning system Perspectives and issues in machine learning, Types of learning. 

Concept learning task, Concept Learning as search through a hypothesis space, Finding maximally specific hypotheses, Version spaces and the candidate elimination algorithm, Inductive Bias

👉UNIT-1 (A) Introduction Notes 

👉UNIT-1(B) Concept Learning PPTs

👉UNIT-1 Introduction and Concept Learning Notes

👉UNIT-1 Introduction and Concept Learning Long Answer Question

Unit - II

Decision Treelearning and Neural Network learning: Decision Tree representation and learning algorithm, appropriate problems for Decision Tree Learning, Hypothesis space search in Decision Tree Learning, Inductive bias in Decision Tree Learning: Occam’s razor, Issues in Decision Tree Learning. 

Introduction to Neural Network Learning, Introduction to Perceptions, Perceptions, Introduction to Gradient Descent and Back propagation.

👉UNIT-2 (A) Decision Tree Learning Notes 

👉UNIT-2 (A) Decision Tree Learning PPTs

👉UNIT-2(A) Decision Tree Learning Long Answer Questions

👉UNIT-2 (B) Introduction to Neural Network Learning Notes

👉UNIT-2 (B) Introduction to Neural Network Learning PPT

👉UNIT-2 (B) Introduction to Neural Network Learning Long Answer Questions

Unit - III

SupportVector Machines: Introduction, Linear Classifier, Non-linear Classifier, Training SVM, Support Vector Regression.

Bayesian Learning: Bayes theorem and concept learning, Minimum Description Length Principle, Bayes optimal classifier, Gibbs Algorithm, Naïve Bayes Classifier, The EM algorithm.

👉UNIT-3 (A) Support Vector Machine Notes

👉UNIT-3 (A) Support Vector Machine PPTs

👉SVM Program

👉Training SVM

👉Extra Info on SVM

Unit – IV

Instance-basedTechniques: Introduction to Instance-based Techniques, Lazy vs. eager generalization, K nearest neighbor, Radial Basis Function, Case Based reasoning, Remarks on Lazy vs Eager learning.



Unit – V

Genetic Algorithm: Biological motivation, Representing Hypothesis, Genetic Operators, Fitness function and selection, Hypothesis space search, Genetic Programming, Models of Evolution and Learning, Parallelizing Genetic Algorithms.


👉YouTube Link1

👉YouTube Link 2


Text Books:

1.     Machine Learning - Mc Graw Hill, Tom M. Mitchell.

Reference Books:

1.     Machine Learning Methods and the Environmental Science, Neural Network, William W Hsieh Cambridge University Press.

2.     Rechard o Duda, Peter E. Hart and David G. Stork, & pattern Classification, John Wiley & Sons Inc,2001

3.     Chris Bishop, Neural Network for, Pattern Recognition, Oxford University Press. 1995

Comments

Post a Comment

Popular posts from this blog

VERSION SPACES AND THE CANDIDATE-ELIMINATION ALGORITHM

-
Image
  VERSION SPACES AND THE CANDIDATE-ELIMINATION ALGORITHM   •         The key idea in the CANDIDATE-ELIMINATION algorithm is to output •         a description of the set of all hypotheses consistent with the training examples Note difference between definitions of consistent and satisfies ·        An example x is said to satisfy hypothesis h when h(x) = 1, regardless of whether x is a positive or negative example of the target concept . ·        An example x is said to consistent with hypothesis h iff h(x) = c(x) The LIST-THEN-ELIMINATION algorithm The LIST-THEN-ELIMINATE algorithm first initializes the version space to contain all hypotheses in H and then eliminates any hypothesis found inconsistent with any training example . ___________________________________________________________________________ 1. VersionSpace c...
Machine Learning

Support Vector Machine (SVM) - ML Program

-
Image
  SVM Classifier for IRIS Data Set Steps: Import the library files Read the dataset (Iris Dataset) and analyze the data Preprocessing the data Divide the data into Training and Testing Build the model - SVM Classifier with different types of kernels Model Evaluation 1.Import the library files 2. Read the dataset (Iris Dataset) and analyze the data 3. Preprocessing the data 4. Divide the data into Training and Testing 5. Build the model -  SVM Classifier with different types of kernels Support vector machines (SVMs)  are a set of supervised learning methods used for  classification ,  regression  and  outliers detection . The advantages of support vector machines are: Effective in high dimensional spaces. Still effective in cases where number of dimensions is greater than the number of samples. Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient. Versatile: different  Kernel functions ...
Machine Learning

Support Vector Machines

-
Image
  Predicting Date Fruit Varieties with Support Vector Machines Context Objective is to leverage advanced machine learning techniques to predict the variety of date fruits, empowering farmers and agricultural stakeholders to improve classification accuracy and streamline post-harvest processes. Your role is to analyze various morphological, colorimetric, and textural attributes of date fruits to build predictive models that distinguish different varieties effectively. Dataset Description You have been provided with a comprehensive dataset containing morphological and colorimetric features of different varieties of date fruits. The dataset includes the following attributes: Morphological Attributes: AREA:  Surface area of the date fruit. PERIMETER:  Perimeter measurement around the fruit. MAJOR_AXIS:  Length of the major axis of the date fruit. MINOR_AXIS:  Length of the minor axis of the date fruit. ECCENTRICITY:  Ratio descri...
Machine Learning