Intelligent Systems and Machine Learning Algorithms (BEC515A)

Intelligent Systems and Machine Learning Algorithms

Course Code BEC515A 
CIE Marks 50
Teaching Hours/Week (L: T:P: S) 3:0:0 
SEE Marks 50
Total Hours of Pedagogy 40 
Total Marks 100
Credits 03 
Exam Hours 03
Examination type (SEE) THEORY

Module-1

Introduction: What is AI? Foundations and History of AI Intelligent Agents: Agents and

environment, Concept of Rationality, The nature of environment, The structure of agents.

Text book 1: Chapter 1- 1.1, 1.2, 1.3 Chapter 2- 2.1, 2.2, 2.3, 2.4

Module-2

Problem‐solving: Problem‐solving agents, Example problems, Searching for Solutions

Uninformed Search Strategies: Breadth First search, Depth First Search, Iterative deepening

depth first search;

Text book 1: Chapter 3- 3.1, 3.2, 3.3, 3.4

Module-3

Informed Search Strategies: Heuristic functions, Greedy best first search, A*search. Heuristic

Functions Logical Agents: Knowledge–based agents, The Wumpus world, Logic, Propositional

logic, Reasoning patterns in Propositional Logic

Text book 1: Chapter 3-3.5,3.6 Chapter 4 – 4.1, 4.2 Chapter 7- 7.1, 7.2, 7.3, 7.4, 7.5

Module-4

Introduction: Machine learning Landscape: what is ML?, Why, Types of ML, main challenges of

ML Concept learning and Learning Problems – Designing Learning systems, Perspectives and

Issues – Concept Learning – Find S-Version Spaces and Candidate Elimination Algorithm –

Remarks on VS- Inductive bias.

Text book 3: Chapter 1, Textbook 4:Chapter 1 and 2

Module-5

End-to-end Machine learning Project: Working with real data, Look at the big picture, Get the

data, Discover and visualize the data, Prepare the data, select and train the model, Fine tune your

model. Classification: MNIST, training a Binary classifier, performance measure, multiclass

classification, error analysis, multi-label classification, multi-output classification

Textbook 4: Chapter 2, Chapter 3

Suggested Learning Resources:

Text Book:

1. Stuart J. Russell and Peter Norvig , Artificial Intelligence, 3rd Edition, Pearson,2015

2. Elaine Rich, Kevin Knight, Artificial Intelligence, 3rd Edition,Tata McGraw Hill,2013.

3. Tom M. Mitchell, Machine Learning, McGraw-Hill Education, 2013

4. Aurelien Geron, Hands-on Machine Learning with Scikit-Learn &Tensor Flow , O’Reilly,

Shroff Publishers and Distributors Pvt. Ltd 2019.

Reference Books:

1. George F Lugar, Artificial Intelligence Structure and strategies for complex, Pearson

Education, 5th Edition, 2011

2. Nils J. Nilsson, Principles of Artificial Intelligence, Elsevier, 1980

3. Saroj Kaushik, Artificial Intelligence, Cengage learning, 2014.

4. Ethem Alpaydin, Introduction to Machine Learning, PHI Learning Pvt. Ltd, 2nd Ed.,

2013

5. T. Hastie, R. Tibshirani, J. H. Friedman, The Elements of Statistical Learning, Springer,

1st edition, 2001

6. Machine Learning using Python, Manaranjan Pradhan, U Dinesh Kumar, Wiley, 2019

7. Machine Learning, Saikat Dutt, Subramanian Chandramouli, Amit Kumar Das,

Pearson,2020