Subjects

Fundamentals of Artificial Intelligence (4 credit)

1.      Introduction

2.      Preliminaries: Algorithms, Complexities, Graphs, Trees

3.      State Space Search: Blind search

4.      State Space Search: Informed search

5.      Genetic Algorithm

6.      Knowledge representation and predicate logic

7.      Dealing with uncertainties: Bayes rule, Certainty Factors

8.      Fuzzy Logic

9.      Neural Networks

10.    Case Study

Introduction to Data Science (4 credit)

1.  Basics of Data Science

2.  Probability and Statistics

3.  Statistical Inference

4.  Regression Analysis

5.  Big Data and Data Science

6.  Getting and Cleaning Data

7.  Visualising the Data: Tableau/PowerBI

8.  Data Science Toolkit: Excel, R, Weka etc.

9.  Ethical Issues in Data Science

10.    Case Study

Python Programming (4 credit)

1.  Preliminaries

2.  Variables, Expressions and Statements

3.  Functions, Conditionals, Recursion, Iteration

4.  Strings, Lists, Tuples, Dictionaries

5.  Files and Error Handling

6.  Introduction to Colab

7.  Computation with Python – NumPy/SciPy

8.  Data Manipulation and Visualisation in Python- Pandas/matplotlib

9.  Introduction to Scikit-learn

10.    Web Scraping in Python – BeautifulSoup

Machine Learning & Applications (4 credit)

1.  Preliminaries

2.  Decision Tree Learning

3.  Unsupervised Learning – Clustering algorithms

4.  Supervised Learning – K-NN/Naïve Bayes

5.  Support Vector Machine

6.  Artificial Neural Networks

7.  Deep Learning concepts

8.  Convolutional Neural Networks

9.  Reinforcement Learning

10.    Case Study

Project on Emerging Areas in AI and Data Science  (8 credit)

Tentative Class Timings:

Theory: 6.30 – 8.30 pm on Friday, 1:30-3:30 pm & 4-6 pm on Saturday (for Module-1) 

Classes in Physical mode (timings may vary from batch to batch)

Class Venue: 

CMATER Classroom (Room No: T-3-11),

Computer Science & Engineering Department,

Prayukti Bhawan (2nd Floor)

Jadavpur University, Kolkata-32