Course Details

JNTUA | CSE - AI & DS | 3 Year | Semester - I

JNTU Anantapur

6 Subjects 0 Lectures

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Subjects Covered | 6

Mining Massive Data Sets

Introduction to Control Systems, OE-I

Computer Networks

Big Data Technologies

Software Engineering for AI

Machine Learning

Mining Massive Data Sets

1.1 PYQ's

2.1 PYQ's

3.1 PYQ's

4.1 PYQ's

5.1 PYQ's

Introduction to Control Systems, OE-I

1.1 PYQ's

2.1 PYQ's

3.1 PYQ's

4.1 PYQ's

5.1 PYQ's

Computer Networks

1.1 PYQ's

2.1 PYQ's

3.1 PYQ's

4.1 PYQ's

5.1 PYQ's

Big Data Technologies

1.1 PYQ's

2.1 PYQ's

3.1 PYQ's

4.1 PYQ's

5.1 PYQ's

Software Engineering for AI

1.1 PYQ's

2.1 PYQ's

3.1 PYQ's

4.1 PYQ's

5.1 PYQ's

Machine Learning

1.1 PYQ's

2.1 PYQ's

3.1 PYQ's

4.1 PYQ's

5.1 PYQ's

JNTUA | CSE - AI & DS | 3 Year | Semester - I

Subjects Covered | 6

Mining Massive Data Sets

1 Introduction to Data Mining & Theory of Map Reduce

2 Mining Data Streams

3 Clustering & Advertising on the Web

4 Mining Social-Network Graphs

5 Large-Scale Machine Learning

Introduction to Control Systems, OE-I

1 Mathematical Models of Physical Systems

2 Time Response Analysis & Root Locus Technique

3 Frequency Response Analysis

4 Stability Concepts & Analysis

5 State Variable Analysis

Computer Networks

1 Computer Networks and The Internet

2 The Data Link Layer, Access Networks, and Lans

3 The Network Layer

4 The Transport Layer

5 Principles of Network Applications

Big Data Technologies

1 Getting an Overview of Big Data

2 Introducing Technologies for Handling Big Data & Understanding Hadoop Ecosystem

3 Understanding Map Reduce Fundamentals and H Base

4 Customizing Map Reduce Execution and Implementing Map Reduce Program & Many Other Topics

5 Exploring Hive

Software Engineering for AI

1 Introduction to Computer Software for AI, AI Problems and Conventional SE Problems, Software Engineering Methodology

2 An Incremental and Exploratory Methodology, New Paradigms for System Engineering

3 Towards a Discipline of Exploratory Programming, Machine Learning: Much Promise, Many Problems

4 Machine Learning and Expert Systems

5 AI into Practical Software

Machine Learning

1 Introduction to Machine Learning & Preparing to Model

2 Modelling and Evaluation & Basics of Feature Engineering

3 Bayesian Concept Learning & Supervised Learning: Classification

4 Supervised Learning: Regression

5 Unsupervised Learning