Short-Term Artificial Intelligence Course
This page provides open educational resources for a Short-Term Course on Artificial Intelligence for Engineering Students, conducted in 2025. The course introduces the foundations of Artificial Intelligence, reasoning and search techniques, machine learning, natural language processing, unsupervised learning, and ethical aspects of AI.
The course combines conceptual foundations with practical examples and Python programming exercises, providing engineering students with an accessible introduction to modern Artificial Intelligence techniques and applications.
Institute: MBM University, Jodhpur
What You Will Find
- Lecture notes covering fundamental concepts of Artificial Intelligence
- Logic, knowledge representation, and reasoning
- State-space representation and search algorithms
- Breadth-First Search, Depth-First Search, and A* search
- Introduction to supervised, unsupervised, and reinforcement learning
- Neural networks, classification, and regression
- Natural Language Processing and text processing
- K-Means clustering and unsupervised learning
- Python programs and practical AI examples
- Self-tests and supporting learning resources
- Social and ethical implications of Artificial Intelligence
Course Information
Course
Short-Term Course on Artificial Intelligence for Engineering Students (2025)
Institute
Prerequisites
Students should have a basic background in mathematics and programming. The recommended prerequisites include:
- Linear Algebra
- Calculus
- Probability and Statistics
- Basic programming concepts including arrays, recursion, sorting, and searching
- Python programming for Artificial Intelligence
- NumPy and pandas
- TensorFlow and PyTorch
- Discrete Mathematics, particularly graphs and logic, is recommended
Learning Outcomes
After completing the course, students should be able to understand the fundamental principles of Artificial Intelligence and apply basic AI techniques to practical problems.
- Understand the foundations, goals, and major subfields of AI.
- Represent knowledge using logic and related techniques.
- Understand reasoning patterns and knowledge bases.
- Apply state-space search techniques to problem-solving.
- Understand supervised, unsupervised, and reinforcement learning.
- Implement basic machine learning techniques using Python.
- Understand fundamental concepts of Natural Language Processing.
- Apply clustering techniques such as K-Means.
- Understand ethical and social issues associated with Artificial Intelligence.
Lecture Notes and Course Modules
Module 1: Foundations of Artificial Intelligence
Topics Covered
- Definition and evolution of Artificial Intelligence
- Turing Test
- Goals and major subfields of AI
- Engineering applications of AI
- Knowledge representation
- Physical Symbol System Hypothesis
- Propositional logic
- Predicate logic
Learning Resources
Module 2: Reasoning Patterns and Search Algorithms
Topics Covered
- Predicate logic and Prolog
- Knowledge bases
- State-space representation
- Breadth-First Search (BFS)
- Depth-First Search (DFS)
- A* search
- Real-world problem solving using Python
Learning Resources
- Lecture 2: Logic and Reasoning | Self Test
- Lecture 3: Search Algorithms
- Python Examples: Robot Path , Grid Load , Shortest Path , CPU Scheduling
Module 3: Machine Learning
Topics Covered
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Neural networks
- Linear classifiers
- Regression models
- Machine learning applications using Python
Learning Resources
Module 4: Natural Language Processing
Topics Covered
- Text processing
- Tokenization
- Part-of-speech tagging
- Parsing
- Text classification
- NLP workflow
- Natural Language Processing applications
- NLP programming using NLTK
Learning Resources
Module 5: Unsupervised Learning and AI Ethics
Topics Covered
- Unsupervised learning
- Clustering techniques
- K-Means algorithm
- Applications of unsupervised learning
- Social implications of Artificial Intelligence
- Ethical issues in Artificial Intelligence
Learning Resources
Python Programs and Practical Examples
The course includes practical Python examples demonstrating Artificial Intelligence concepts and algorithms.
About the Course
This short-term course was designed to provide engineering students with a compact introduction to Artificial Intelligence and its applications. The course combines theoretical foundations with hands-on programming exercises so that students can connect AI concepts with practical problem-solving.
The lectures progress from fundamental AI concepts and logical reasoning to search algorithms, machine learning, Natural Language Processing, unsupervised learning, and ethical considerations.
Related Computer Science Resources
Students interested in further study may also explore related Computer Science courses and learning resources available on this website.
Further Learning
Students who wish to continue their study of Artificial Intelligence can explore more comprehensive lecture notes, textbooks, algorithms, machine learning methods, Natural Language Processing, intelligent agents, and other advanced AI topics.
The short-term course is intended as an introductory foundation and can be followed by more advanced study in machine learning, deep learning, knowledge representation, computer vision, NLP, and intelligent systems.