CS604PC: Machine Learning Lab CSE Syllabus for B.Tech 3rd Year 2nd Sem R18 Regulation JNTUH

Machine Learning Lab detailed Syllabus for Computer Science & Engineering (CSE), R18 regulation has been taken from the JNTUH official website and presented for the students affiliated to JNTUH course structure. For Course Code, Subject Names, Theory Lectures, Tutorial, Practical/Drawing, Credits, and other information do visit full semester subjects post given below. The Syllabus PDF files can also be downloaded from the universities official website.

For all other CSE 3rd Year 2nd Sem Syllabus for B.Tech R18 Regulation JNTUH, do visit CSE 3rd Year 2nd Sem Syllabus for B.Tech R18 Regulation JNTUH Subjects. The detailed Syllabus for machine learning lab is as follows.

Course Objectives:

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Course Outcomes:

After the completion of the course the student can able to:

1. understand complexity of Machine Learning algorithms and their limitations;
2. understand modern notions in data analysis-oriented computing;
3. be capable of confidently applying common Machine Learning algorithms in practice and implementing their own;
4. Be capable of performing experiments in Machine Learning using real-world data.

List Of Experiment:

1. The probability that it is Friday and that a student is absent is 3 %. Since there are 5 school days in a week, the probability that it is Friday is 20 %. What is theprobability that a student is absent given that today is Friday? Apply Bayes rule in python to get the result. (Ans: 15%)
2. Extract the data from database using python
3. Implement k-nearest neighbours classification using python
4. Given the following data, which specify classifications for nine combinations of VAR1 and VAR2 predict a classification for a case where VAR1=0 . 906 and VAR2=0 . 606, using the result of k-means clustering with 3 means (i.e., 3 centroids)
   Var1 Var2 Class
   1 . 713 1 . 586 0
   0 . 180 1 . 786 1
   0 . 353 1 . 240 1
   0 . 940 1 . 566 0
   1 . 486 0 . 759 1
   1 . 266 1 . 106 0
   1 . 540 0 . 419 1
   0 . 459 1 . 799 1
   0 . 773 0 . 186 1
5. The following training examples map descriptions of individuals onto high, medium and low credit-worthiness.
   medium skiing design single twenties no -> highRisk
   high golf trading married forties yes -> lowRisk
   low speedway transport married thirties yes -> medRisk
   medium football banking single thirties yes -> lowRisk
   high flying media married fifties yes -> highRisk
   low football security single twenties no -> medRisk
   medium golf media single thirties yes -> medRisk
   medium golf transport married forties yes -> lowRisk
   high skiing banking single thirties yes -> highRisk
   low golf unemployed married forties yes -> highRisk
   Input attributes are (from left to right) income, recreation, job, status, age-group, home-owner. Find the unconditional probability of ‘golf and the conditional probability of ‘single’ given ‘medRisk’ in the dataset?
6. Implement linear regression using python.
7. Implement Naive Bayes theorem to classify the English text
8. Implement an algorithm to demonstrate the significance of genetic algorithm
9. Implement the finite words classification system using Back-propagation algorithm

For detail Syllabus of all other subjects of B.Tech 3rd Year Computer Science & Engineering, visit CSE 3rd Year Syllabus Subjects.

For all B.Tech results, visit JNTUH B.Tech all years, and semester results from direct links.