ELE 01 ARTIFICIAL INTELLIGENCE
Course Code & Title ARTIFICIAL INTELLIGENCENG
Type of Course Major Semester 1 Academic Level 100 – 199
Course Details Credit Lecture Tutorial Practical Total
per week per week per week Hours
4 3 - 2 75
Pre-requisites Fundamentals of Artificial Intelligence
Course Summary This Artificial Intelligence course provides a comprehensive overview of AI
concepts, covering foundational topics such as problem-solving, search algorithms,
and knowledge representation, followed by core areas like machine learning, deep
learning, natural language processing, and computer vision. It also includes
reinforcement learning and practical training in popular AI tools and frameworks like
Python, TensorFlow, and PyTorch. Ethical considerations, real-world applications,
and a capstone project help students develop both theoretical understanding and
hands-on skills, preparing them to build intelligent systems across various domains.
Course Outcomes (CO):
CO CO Statement Cogni Knowle Evaluation Tools
tive dge used
Leve Catego
l* ry#
CO Understand the fundamental concepts, history, U C Instructor created
1 and applications of Artificial Intelligence across exams / Quiz
various domains.
CO Apply problem-solving techniques using Ap P Practical Assignment /
2 search algorithms and evaluate their efficiency Observation of
in solving AI problems. Practical Skills
CO Apply problem-solving techniques using Ap P Practical Assignment /
search algorithms and evaluate their efficiency Observation of
in solving AI problems. Practical Skills
CO Analyze and implement basic machine learning Ap C Practical Assignment /
4 algorithms for classification, regression, and Observation of
clustering tasks. Practical Skills
CO Design and develop neural networks and deep Ap P Practical Assignment /
5 learning models for real-world data analysis. Observation of
Practical Skills
CO Apply natural language processing techniques Ap P Practical Assignment /
6 for text understanding and language modeling. Observation of
Practical Skills
Module Unit Content Hours Marks
I INTRODUCTION TO ARTIFICIAL INTELLIGENCE 10
1 Introduction to AI 2
2 Definition and history of AI 2
3 Agent and environment 3
4 Concept of AI, ML and DL 3
II INTRODUCTION TO ML and DL 10
5 Introduction to ML and DL 1
6 Essential concepts of ML and DL 2
7 Types of learning 2
8 Perceptron and neutral network 2
9 Big data management 3
III INTRODUCTION TO AI & ROBOTICS 15
9 Introduction to AI & Robotics 1
10 Robot locomotion:-legged, wheeled and Areal mobile robot 3
11 Sensors for mobile robot 2
13 Computer vision for robot 2
14 Language processing for robot 2
IV ETHICS OF ROBOTIC AND APPLICATIONS 10
16 Role of AI in human life 3
17 Applications of AI 3
18 Understanding ethics in AI 1
19 AI governance by human right 2
20 Teaching machines to be moral 1
V EXERCISES INCLUDED FOR PRACTICE 30
21 Problem with machine learning algorithm
22 To detect spam mail
23 To implement facial recognition application with artificial
neural network
References:
1. Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.).
Pearson.
2. Alpaydin, E. (2020). Introduction to Machine Learning (4th ed.). MIT Press.
3. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
4. Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.
5. Rich, E., Knight, K., & Nair, S. B. (2008). Artificial Intelligence (3rd ed.). McGraw-
Hill.
Raschka, S., & Mirjalili, V. (2019). Python Machine Learning (3rd ed.). Packt
Publishing.
6. Ng, A. (n.d.). AI for Everyone [Online Course]. Coursera.
https://www.coursera.org/learn/ai-for-everyone
Google AI. (n.d.). AI Education. https://ai.google/education/
Fast.ai. (n.d.). Practical Deep Learning for Coders. https://www.fast.ai/
7. MIT OpenCourseWare. (n.d.). Introduction to Deep Learning.
http://introtodeeplearning.com/
Mapping of CO’s with :
PS PSO PSO PSO PSO PSO PO PO2 PO3 PO4 PO5 PO6
O1 2 3 4 5 6 1
- - 1 1 - -
CO 1
- 1 2 2 - -
CO 2
- 1 3 3 - -
CO 3
- 1 2 2 - -
CO 4
- 2 2 2 - -
CO 5
- 1 3 3 1 1
CO 6
Correlation Levels:
Level Correlation
- Nil
1 Slightly / Low
2 Moderate / Medium
3 Substantial / High
Assessment Rubrics:
▪ Assignment / Seminar/ Lab assignments (Practical)
▪ Midterm Exam
▪ Programming Assignments (20%)
▪ Final Exam (70%)
Mapping of CO’s to Assessment Rubrics:
Internal Assignme Project End Semester
Examinatio nt Examination
n
CO ✓ ✓ ✓
1
CO ✓ ✓ ✓
2
CO ✓ ✓ ✓
3
CO ✓ ✓ ✓
4
CO ✓ ✓ ✓
5
CO ✓
6
