Course Code: AI203
Course Title : Mathematics for Data Science
Pre-requisite(s):
Co-requisite(s): AI204 Mathematics for Data Science Lab
Credits: L:3 T:O P :0
Classes schedule per week:3
Class: B.Tech
Semester/Level: IV/2
Branch:AIML
Course Outcome:
On completion of the course students shall be:
1) Able to formulate practical problems as graphs and use of graph theoretic methods on them.
2) Able to model transfer of information and state using Markov chains.
3) Able to reduce the dimensionality of large datasets and understand the challenges in doing so.
4) Able to interpolate missing data both for linear and non linear cases over multiple dimensions.
5) Apply concepts of optimization to machine learning problems.
Module 1: Graph Theory Lectures: 8
Basic concepts and terminology,Adjacency matrix-representation and uses,Eigen values and Eigen
vectors-importance and applications
Module 2: Stochastic Processes Lectures: 8
Markov chain basics, Hidden Markov models,-the Likelihood problem, the decoding problem,the
learning problem.
Module 3: SVD & PCA Lectures: 8
ASVectors and linear product spaces.Singular values,Singular Value Decomposition, Compression of data
using PCA, relation of PCA ,relatiob of PCA with co-variance and correlation,Linear Discrimation Analysis
Module 4: Interpolation Lectures: 8
Lagrange interpolation, Orthogonal family of polynomials, Newton divided difference methods-use of
Vandermonde matrix , Chebyshev interpolation,Hernite regression , Least square regression
Module 5: Optimization and Learning Techniques Lectures: 8
Basic probability theory, Introoduction to matrix calculus-Matrix differentiation,Matrix integration
,Maximum Likelihood Estimation,Gradient Descent
Textbook:
1) Arangaia C,”Linear Algebra with Machine Learning and data”,CRC Press,1st Edition 2023
Reference Books:
1) Carter N. “Data Science for Mathematics”, CRC Press, 1st Edition 2021
2) Strang G.”Introduction to Linear Algebra”, 5th Edition ,Cambridge Press,2016
Course Code: AI201
Course Title : Probability and Statistics
Pre-requisite(s): Mathematics-1,2
Co-requisite(s):
Credits: L:3 T:O P :0
Classes schedule per week:3
Class: B.Tech
Semester/Level: IV/2
Branch:AIML
Course Outcome:
On completion of the course students shall be:
1) Able to describe and summarize large real life datasets.
2) Able to compute the probability of complex events.
3) Able to describe the characteristic properties of different types of random variables.
4) Able to estimate distributition parameters for real life samples.
5) Able to perfom hypothesis testing on datasets and interpret the results.
Module 1: Descriptive Statistics Lectures: 8
Introduction,Describing Datasets,Summarizing Datasets,Chebyshev’s Inequality, Normal Datasets,Paired
Datasets and Correlation coefficient, Lorenz Curve and Gini Index
Module 2: Elements of Probability Lectures: 8
Basic concepts,Conditional Probability,Baye’s formula,Independent Events, Random variables,Types of
Random variables,Jointly distributed random variables,Expectatioon and its properties,Variance and
Covariance
Module 3: Special Random Variables Lectures: 8
The Bernouli and binomial Random Variables, The Poisson Random Variables,The uniform Random
Variables,The normal Random Variables,Exponential Random Variables,The gamma distribution,The Chi-
square,t-distribution and F-distribution.
Module 4: Parameter Estimation Lectures: 8
Maximum likelihood technique Estimatiors, Interval Estimates, Estimating the difference in means of two
normal distributions
Module 5: Hypothesis Testing Lectures: 8
Significance levels,Tets about mean of a normal population,Testing the equality of means of two normal
populations,Tests Significance levels,Tets about mean of a normal population,Testing the equality of
means of two normal populations,Tests concerning the variance of a normal population
Textbook:
1) Ross Sheldon M.,Inroduction to Probability and statistics for Engineers and Scientists, 6th
Edition,Academic Press,2021
Reference Books:
2) Rohatag V.K.,Saleh A.K.,An Introduction to Probability and Statistics, 3rd Edition ,Wiley,2015
(CS241) DESIGN AND ANALYSIS OF ALGORITH
Module I :
Algorithms and Complexity
Introduction, Algorithn Complexity and various cases using Insertion Sort. Asymptotic Notations, Time
complexity of Recursive Algorithm, Solving Recurrences using Iterative, Recursion Tree and Master
Theorem. (8L)
Module II :
Divide and Conquer
Discussion of basic approach using Binary Search, Merge Sort , Quick Sort , Selection in Expected linear
time , Maximum Subarray , Matrix Multiplication , Introduction of Transform and Conquer and A VL
Tree. (8L)
Module III :
Dynamic Programming
Introduction and Approach, Rod Cutting, LCS, Optimal BST, Transitive closure and All-pair Shortest Path,
Travelling Salesperson Problem. (8L)
Module IV :
Greedy and other Design Approaches
Introduction to greedy using fractional knapsack, Huffiman Code, Minimum Spanning Tree Prim and
Kruskal, Single Source Shortest Path Dijkstra's and Bellman-Ford, Introduction to Backtracking using N-
Queens problem, Introduction to Branch and Bound using Assignment Problem or TSP. (8L)
Module V :
NP Completeness and Other Advanced Topics
Non-deterministic algorithms searching and sorting, Class P and NP, Decision and Optimization
problem, Reduction and NPC and NPH, NP Completeness proof for: SAT, Max Clique, Vertex x Cover,
Introduction to Randomized Algorithms, Introduction to Approximation Algorithms. (8L)
Text Book:
1. Cormen Thomas H. et al., Introduction to Algorithms. 3rd Edition, PHI Learning, latest edition.(TI)
(EC261)DIGITAL COMPUTER ELECTRONICS
(NO. OF
MODULE LECTURE
HOURS)
MODULE – I
Implementation of various logic gates and their applications: AND, OR, NAND, NOR, NOT,
EX-OR and EX-NOR gates, Design of ADDER, SUBTRACTOR, BCD Adder, 1 bit ALU, 8
ENCODER, DECODER using Logic gates. Example of various logic implemented in
Mechanical, Electrical and Electronic Systems for ON/OFF control.
Module-II
Implementation of various flip flops and their applications: RS, JK and D Flip Flops, 8
Registers, SISO, PIPO registers with Load and Enable control lines, Counters, Controlled
binary counter, Ring Counter, UP/DOWN Counters, Modulo 10 Counter. Examples of
Counting of Events, display of count in Seven Segment display system, Alarm generation
with terminal counts.
Module- III
8
Design of Memory Elements: ROM, PROM and EPROM, BUS ORGANISED COMPUTERS,
RAM, Schematic diagram of a RAM CHIP, Functions of a Various pins of a Memory chip.
Module – IV
8
Simple as possible computer architecture, concepts with stress on timing diagrams,
Instruction and Opcodes, Microinstructions, Opcode recn h and Execution cycle,
Hardware control Matrix, Macroinstructions, Microprogramming, Bus concepts.
Module-V
8
Data acquisition system and Data logger. Implementation of ON/OFF control and
continuous control using Microprocessors and Microcontrollers.
Text Books:
1) Computer Electronics, 2/e. by A. P. Malvino.
2) "Computer-Based Industrial Control", by Krishna Kant, PHI.
Course code:AI205
Course title:Introduction to Artificial Intelligence
Pre-requisite(s):
Co- requisite(s):
Credits: L:3 T:1 P:0
Class schedule per week: 4
Class: B. Tech
Semester / Level: III
Branch: CSE/IT/AIML
Course Outcomes:
On completion of the course students shall be:
1) Able to formulate propositional logic for real life problems and present formal proofs for standard
problems.
2) Solve problems using First Order Predicate Logic and work with automatic Theorem Provers.
3) Perform Search using Classical and Heuristic methods and solve game problems.
4) Design entropy based solutions for real life probabilistic problems.
5) Understand the basics of reinforcement systems and their operating principals.
Module 1: Introduction and Propositional Logic Lectures: 8
What Is Artificial Intelligence? Agents, Knowledge-Based Systems, Propositional Logic – Syntax,
Semantics, Proof Systems, Resolution, Horn Clauses, Computability and Complexity
Module 2: First Order Predicate Logic Lectures: 8
Syntax, Semantics, Quantifiers and Normal Forms, Proof Calculi, Resolution, Automated Theorem
Provers, Applications and Limitations
Module 3: Search Games and Problem Solving Lectures: 8
Introduction, Uninformed Search, Heuristic Search, Games with Opponents, Heuristic Evaluation
functions
Module 4: Reasoning with Uncertainity Lectures: 8
Computing with Probabilities, The principal of Maximum Entropy, Reasoning with Bayesian
Networks
Module 5: An Introduction to Reinforcement Learning Lectures: 8
Definitions, Uninformed combinatorial search, Value iteration and Dynamic Programming, Q-
Learning, Exploration and Exploitation, Approximation, Generalization and Convergence, the
Curse of Dimensionality
Textbook:
1 Ertel W.., “Introduction to Artificial Intelligence”, UTiCSSpringer, 2nd Edition, 2017
Reference Books:
1) Russell S., Norvig P. “Artificial Intelligence: A Modern Approach”, Pearson Publications, 4 thtEdition,
2022
2) AkerkarR. “Introduction to Artificial Intelligence”, 2nd Edition, PHI Press, 2014
