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
I
MA101BS
Mathematics – I
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I
AP102BS
Applied Physics
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I
CS103ES
Programming for Problem Solving
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I
ME104ES
Engineering Graphics
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I
AP105BS
Applied Physics Lab
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I
CS106ES
Programming for Problem Solving Lab
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I
MC109ES
Environmental Science
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II
MA201BS
Mathematics – II
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II
CH202BS
Chemistry
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II
EE203ES
Basic Electrical Engineering
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II
ME205ES
Engineering Workshop
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II
EN205HS
English
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II
CH206BS
Engineering Chemistry Lab
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II
EN207HS
English Language and Communication Skills Lab
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II
EE208ES
Basic Electrical Engineering Lab
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II-I
CS310PC
Discrete Mathematics
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II-I
CS302PC
Data Structures
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II-I
MA313BS
Mathematical and Statistical Foundations
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II-I
CS304PC
Computer Organization and Architecture
II-I
CS311PC
Python Programming
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II-I
SM306MS
Business Economics & Financial Analysis
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II-I
CS307PC
Data Structures Lab
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II-I
CS312PC
Python Programming Lab
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II-I
MC309
Gender Sensitization Lab
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II-II
CS416PC
Formal Language and Automata Theory
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II-II
CS417PC
Software Engineering
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II-II
CS403PC
Operating Systems
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II-II
CS404PC
Database Management Systems
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II-II
CS412PC
Object Oriented Programming using Java
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II-II
CS406PC
Operating Systems Lab
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II-II
CS407PC
Database Management Systems Lab
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II-II
CS408PC
Java Programming Lab
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II-II
MC409
Constitution of India
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III-I
Design and Analysis of Algorithms
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III-I
Machine Learning
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III-I
Computer Networks
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III-I
Compiler Design
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III-I
Graph Theory (PE1)
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III-I
Introduction to Data Science(PE1)
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III-I
Web Programming(PE1)
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III-I
Image Processing(PE1)
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III-I
Computer Graphics(PE1)
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III-I
Software Testing Methodologies(PE2)
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III-I
Information Retrieval Systems(PE2)
III-I
Pattern Recognition(PE2)
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III-I
Computer Vision and Robotics(PE2)
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III-I
Data Warehousing and Business Intelligence(PE2)
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III-I
Machine Learning Lab
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III-I
Computer Networks Lab
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III-I
Advanced Communication Skills Lab
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III-I
Intellectual Property Rights
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III-II
Artificial Intelligence
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III-II
DevOps
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III-II
Natural Language Processing
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III-II
Internet of Things(PE3)
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III-II
Data Mining(PE3)
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III-II
Scripting Languages(PE3)
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III-II
Mobile Application Development(PE3)
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III-II
Cryptography and Network Security(PE3)
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III-II
Artificial Intelligence and Natural Language
Processing Lab
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