Dr. Mahalingam College of Engineering and Technology - B.Tech. Artificial Intelligence and Data Science - Syllabus and Material Available

 

Dr. Mahalingam College of Engineering and Technology

(An Autonomous Institution)

                      Pollachi - 642 003

 

B.Tech. Artificial Intelligence and Data Science

 

                           Semesters III

 

REGULATIONS 2019

Syllabus

 

Probability and Statistics for Data Science

 

Unit I            Probability and Random Variables                                                            9+3 Hours 

Axioms of Probability- Conditional Probability- Total Probability -Baye‟s Theorem- Random Variables- Probability Mass Function- Probability Density Functions- Properties - Moments- Moment generating functions and their properties- Binomial- Poisson- Uniform –Exponential- Normal Distributions and their properties.  

 

Unit II       Two Dimensional Random Variables                                                            9+3 Hours 

Joint distributions – Marginal and conditional distributions – Covariance – Correlation and linear regression using least square method – Transformation of random variables.  

 

Unit III      Testing of Hypotheses                                                                                       9+3 Hours

Sampling Distributions- Estimation of parameters-Testing of hypotheses for mean, variance, proportions and differences using Normal, t, Chi-Square and F distributions – Tests for independence of attributes and Goodness of fit.  

UNIT IV      Design of Experiments                                                                                    9+3 Hours

Analysis of Variance (ANOVA)- One way Classification – Completely Randomized Design(CRD) – Two way Classification – Randomized Block Design (RBD) – Latin square.  

Unit V        Statistical Quality Control                                                                              9+3 Hours

Control charts for measurements(X and R Charts)-Control charts for attribute

s (p,c and np charts

–Tolerance limits-Acceptance sampling. 

 

Data Structures and Algorithm Analysis –I  

 

Unit I            Basic Concepts of Algorithms   8 Hours

Introduction – Classification of Data Structures – Abstract data type – Algorithm properties – Fundamentals of Algorithmic Problem Solving – Fundamentals of analysis framework – Efficiency classes – Asymptotic notations.  

 

Unit II            List                                                                                                                9 Hours

List – Array implementation – Linked List implementation:Singly, Doubly,

 Circular Linked list – Operations: Insert, Delete and Search- Applications of List. 

 

Unit III          Stack and Queue                                                                                       9 Hours

Stack – Implementation – Applications: Balancing Symbols, Infix to Postfix conversion, Evaluation of Postfix expression and function calls – Queue – Implementation –Circular Queue- Deque – Applications.  

 

Unit IV           Hashing and Mathematical Analysis of Algorithms                     10 Hours

Hashing – Separate chaining – Open addressing – Double hashing – Rehashing. Mathematical analysis of non-recursive algorithms: Matrix Multiplication – Mathematical analysis of recursive algorithms: Factorial problem, Towers of Hanoi – Empirical analysis of algorithms.  

Unit V            Simple Algorithmic Design Techniques                                             9 Hours

Brute force approach: Exhaustive Search – String matching:Naive approach, Linear search Bubble sort – Divide and Conquer technique: Binary search, Merge sort, Quick sort.  

 

 

 

Computer Architecture

 

Unit I            Memory Organization and Addressing                                            9 Hours

Evolution of Microprocessor – Basic Processor Architecture –

 Operational concepts – Performance

– Memory location – Memory Operations – Instructions and sequencing – Addressing modes – CISC Vs RISC – DMA. 

 

Unit II            Input / Output and Basic Processing Unit                                       9 Hours Accessing I/O devices – Interrupts – Buses – Instruction Execution – Hardware Components – Instruction Fetch and Execution Steps – Control Signals – Hardwired Control, CISC Style Processors: Micro programmed Control. 

Unit III          Cache Memory Design                                                                            9 Hours 

Characteristics of Memory Systems – Cache Memory Principles – Elements of Cache Design – Mapping Function – Example of Mapping Techniques – Replacement Algorithms –  

Performance Consideration.  

Unit IV          Pipelining                                                                                                   9 Hours

Pipelining Concept – Pipeline Organization and issues- Data Dependencies –

Memory Delays  

– Branch Delays – Resource Limitations – Performance Evaluation – Superscalar operation– Pipelining in CISC Processors  

Unit V            Parallelism                                                                                                  9 Hours

Instruction Level Parallelism – Parallel Processing Challenges – Flynn‟s Classification – Hardware multithreading – Multicore Processors: GPU, Multiprocessor Network Topologies – Case Study: ARM, Intel 32/64. 

 

Data Mining

 

Unit I              Introduction                                                                                              10 Hours

Introduction to Data Mining: Kinds of Data – Kinds of Patterns – Technologies - Applications – Issues - Data Objects and Attribute Types, Basic Statistical Descriptions of  Data, Data Visualization, Measuring Data Similarity - Preprocessing: Data Quality - Major Tasks in Data Preprocessing - Data Reduction – Data Transformation and Data Discretization - Data Cleaning and Data Integration. 

Unit II             Data Warehousing                                                                                    8 Hours

Data Warehousing and Online Analytical Processing: Data Warehouse basic concepts - Data Warehouse Modeling - Data Cube and OLAP - Data Warehouse Design and Usage - Data Warehouse Implementation - Data Generalization by Attribute-Oriented Induction. Unit III                        Association                                                                                                   9 Hours

Mining Frequent Patterns - Associations and Correlations: Basic Concepts and Methods: Frequent Item set Mining Methods, Pattern Evaluation Methods, Frequent Pattern and Association Mining: A Road Map, Multidimensional Space, Constraint-Based Frequent Pattern Mining, Applications of frequent pattern Mining.  

Unit IV           Classification and Clustering                                                               10 Hours 

Classification: Basic Concepts - Decision Tree Induction – Bayes Classification Methods – Rule Based Classification – K-Nearest-Neighbor Classifier - Model Evaluation and Selection

– Techniques to Improve Classification Accuracy. Cluster Analysis:

 Basic Concepts and Methods- Cluster Analysis - Partitioning Methods - Hierarchical Methods - Density-Based Methods - Grid-Based Methods.  

UNIT V           Data Mining Trends                                                                                  8 Hours

Mining Complex Data Types - Statistical Data Mining - Views on Data Mining Foundations - Visual and Audio Data Mining - Data Mining Applications - Data Mining and Society - Data Mining Trends. 

 

Database Systems

 

Unit I              Foundations of DBMS                                                                              7 Hours

File System – Database System – File System Vs. DBMS – Roles in DBMS Environment 

– Data Models and Conceptual Modeling – Functions of DBMS – Components of DBMS – Multi user DBMS Architecture.  

Unit II            Relational Model, ER Model and Normalization                          10 Hours Relational Model: Terminology, Integrity Constraints – Relational Algebra – ER Modeling: Concepts, Relationship Types, Attributes, Structural Constraints – Normalization: Data Redundancy and Update Anomalies, Functional Dependencies,1NF, 2NF, 3NF, BCNF.  

Unit III           SQL Fundamentals                                                                                  10 Hours

SQL: Overview of Query Language, Data Types, Data Definition, Views, Access Control – Data Manipulation – Joins – Nested Queries. 

Unit IV          Advanced SQL and Query Processing                                                  9 Hours

Advanced SQL: Functions and procedures, Cursors, Triggers – Accessing SQL from a Programming Language – Query Processing: Decomposition, Heuristical Approach to Query Optimization, Cost Estimation for Relational Algebra Operations.  

Unit V            Transaction and Concurrency Control                                                 9 Hours

Transaction: Properties – Concurrency Control: Locking methods, Deadlock, Timestamp Ordering, Multi-version Timestamp Ordering, Optimistic Techniques – Database Recovery: Transaction and Recovery, Recovery facilities, Recovery Techniques.  

 

 

 

Materials Available

Contact:

Ramanujam Coaching Centre(R2C)

Ph:7904189145