JNTUH CSE-AIML MACHINE LEARNING LAB SYLLABUS

JNTUH CSE-AIML MACHINE LEARNING LAB SYLLABUS

Course Objective: The objective of this lab is to get an overview of the various machine learning
techniques and can able to demonstrate them using python.
Course Outcomes: After the completion of the course the student can able to:
 understand complexity of Machine Learning algorithms and their limitations;
 understand modern notions in data analysis-oriented computing;
 be capable of confidently applying common Machine Learning algorithms in practice and implementing their own;
 Be capable of performing experiments in Machine Learning using real-world data.
List of Experiments
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 the probability that a student is
absent given that today is Friday? Apply Baye’s rule in python to get the result. (Ans: 15%)
2. Extract the data from database using python
3. Implement k-nearest neighbor’s 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 kmeans 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 Naïve 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

CSE-AIML

SEMESTER SUBJECT CODE SUBJECT Lession Plan Lecturer Notes & Question Bank SYLLABUS ABOUT TUTOR
I MA101BS Mathematics – I Click Here
I AP102BS Applied Physics Click Here
I CS103ES Programming for Problem Solving Click Here
I ME104ES Engineering Graphics Click Here
I AP105BS Applied Physics Lab Click Here
I CS106ES Programming for Problem Solving Lab Click Here
I MC109ES Environmental Science Click Here
II MA201BS Mathematics – II Click Here
II CH202BS Chemistry Click Here
II EE203ES Basic Electrical Engineering Click Here
II ME205ES Engineering Workshop Click Here
II EN205HS English Click Here
II CH206BS Engineering Chemistry Lab Click Here
II EN207HS English Language and Communication Skills Lab Click Here
II EE208ES Basic Electrical Engineering Lab Click Here
II-I CS310PC Discrete Mathematics Click Here
II-I CS302PC Data Structures Click Here
II-I MA313BS Mathematical and Statistical Foundations Click Here
II-I CS304PC Computer Organization and Architecture Click Here
II-I CS311PC Python Programming Click Here
II-I SM306MS Business Economics & Financial Analysis Click Here
II-I CS307PC Data Structures Lab Click Here
II-I CS312PC Python Programming Lab Click Here
II-I MC309 Gender Sensitization Lab Click Here
II-II CS416PC Formal Language and Automata Theory Click Here
II-II CS417PC Software Engineering Click Here
II-II CS403PC Operating Systems Click Here
II-II CS404PC Database Management Systems Click Here
II-II CS412PC Object Oriented Programming using Java Click Here
II-II CS406PC Operating Systems Lab Click Here
II-II CS407PC Database Management Systems Lab Click Here
II-II CS408PC Java Programming Lab Click Here
II-II MC409 Constitution of India Click Here
III-I Design and Analysis of Algorithms Click Here
III-I Machine Learning Click Here
III-I Computer Networks Click Here
III-I Compiler Design Click Here
III-I Graph Theory (PE1) Click Here
III-I Introduction to Data Science(PE1) Click Here
III-I Web Programming(PE1) Click Here
III-I Image Processing(PE1) Click Here
III-I Computer Graphics(PE1) Click Here
III-I Software Testing Methodologies(PE2) Click Here
III-I Information Retrieval Systems(PE2) VIJAYANAND S
III-I Pattern Recognition(PE2) Click Here
III-I Computer Vision and Robotics(PE2) Click Here Click Here
III-I Data Warehousing and Business Intelligence(PE2) Click Here
III-I Machine Learning Lab Click Here
III-I Computer Networks Lab Click Here
III-I Advanced Communication Skills Lab Click Here
III-I Intellectual Property Rights Click Here
III-II Artificial Intelligence Click Here Click Here
III-II DevOps Click Here Click Here
III-II Natural Language Processing Click Here Click Here
III-II Internet of Things(PE3) Click Here Click Here
III-II Data Mining(PE3) Click Here Click Here
III-II Scripting Languages(PE3) Click Here Click Here
III-II Mobile Application Development(PE3) Click Here Click Here
III-II Cryptography and Network Security(PE3) Click Here Click Here
III-II Artificial Intelligence and Natural Language Processing Lab Click Here Click Here
III-II DevOps Lab Click Here Click Here
IV-I Neural Networks & Deep Learning Click Here Click Here
IV-I Reinforcement Learning Click Here Click Here
IV-I Quantum Computing(PE4) Click Here Click Here
IV-I Expert Systems(PE4) Click Here Click Here
IV-I Cloud Computing(PE4) Click Here Click Here
IV-I Game Theory(PE4) Click Here Click Here
IV-I Mobile Computing(PE4) Click Here Click Here
IV-I Expert Systems(PE4) Click Here Click Here
IV-I Cloud Computing(PE4) Click Here Click Here
IV-I Game Theory(PE4) Click Here Click Here
IV-I Mobile Computing(PE4) Click Here Click Here
IV-I Social Network Analysis(PE5) Click Here Click Here
IV-I Federated Machine Learning(PE5) Click Here Click Here
IV-I Augmented Reality & Virtual Reality(PE5) Click Here Click Here
IV-I Web Security(PE5) Click Here Click Here
IV-I Ad-hoc & Sensor Networks Click Here Click Here
IV-I Deep Learning Lab Click Here Click Here
IV-II Organizational Behaviour Click Here Click Here
IV-II Speech and Video Processing(PE6) Click Here Click Here
IV-II Robotics Process Automation(PE6) Click Here Click Here
IV-II Randomized Algorithms(PE6) Click Here Click Here
IV-II Cognitive Computing(PE6) Click Here Click Here
IV-II Semantic Web(PE6) Click Here Click Here

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