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Machine Learning


 Machine Learning


 Course Outcomes:

 Upon completion of the subject, students will be able to

     1.Apply the concepts of concept learning to solve well posed problems. L3

2. Apply decision trees learning, artificial neural networks and evaluation hypotheses for     the machine learning problems. L3

3. Compare and contrast Bayesian, Computational and Instance-based learning techniques. L3

4. Choose correct learning set of rules for machine learning problems using analytical learning.L3

5. Apply inductive and analytical approaches to learn reinforcement learning and Q learning.L3

___________________________________________________________________________________

For More Understanding, Students can refer to the below material. This is for student(s) reference only.

1. Text Book TOM M. MITCHELL -- Download

2. Lesson Plan ----- Download

3. Unit Wise Question Bank- Download

4. Tennis Dataset

5. Salary Dataset


TopicNotesPPTVideo Link Exercise
UNIT 1Download PPTIntroduction to Machine Learning
Types of Learning
Types of Learning
Find-s AlgorithmDownload PPTFind-s AlgorithmDownload 
Candidate Elimaination AlgorithmDownload Candidate Elimaination Algorithm
Hypothesis Space and Inductive BiasDownload PPTHypothesis Space and Inductive Bias
UNIT 2Download
Activation FunctionDownload
Decision Tree LearningDownloadPPTDecision Tree LearningDownload
ID3
PPTID3 example
Evaluation HypothesesDownloadEvaluation Hypotheses
Multilayered NetworksDownload MLP
NN-representationDownloadPPT1
PPT2		1. NN_by-IBM
2. Intro to NN_IITKGP
3. Intro to ANN_IIT
4. NN_representation
ID3
PPTID3 example
Evaluation HypothesesDownloadEvaluation Hypotheses
Multilayered NetworksDownload MLP
NN-representationDownloadPPT1
PPT2		1. NN_by-IBM
2. Intro to NN_IITKGP
3. Intro to ANN_IIT
4. NN_representation
UNIT 3DownloadBayes1. Bayesian Learning-1 
2. Naive Bayes
3. Bayesian Network
4. Python Exercise on NB
5. KNN


Exercise
UNIT 4DownloadBayes1.  Prolog EBG 
2. Deductive Learning
3. Knowledge Learning



Exercise



Machine Learning lab - R22 - LAB MANUAL


ExperimentNameDatasetPy Code/PPTExercise
Day 1 Python Basics   Lab 1- Basics            
EXP 1            
EXP 2 Study of Python Basic LibrariesStatistics, Math, Numpy & Scipy, 
EXP 3 Study of Python Libraries for ML application          Pandas, Matplotlib 
EXP4 Simple Linear Regression  Salary_Data.csv  Linear Regression             
EXP 5  MLR for House Price Prediction using sklearn1. housing
2. MiniHousing		            MLR Code
EXP 6
EXP 7 
EXP 8 
EXP 9 

EXP 10             
EXP 11                                         

1. Find-S Algorithm  Download
2. CEA                       Download
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