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English
Linear: Sequential Order
Course code: CAI-01
Course Name: Emerging Technologies and Applications-Artificial Intelligence (CAI-01)
INSTRUCTIONAL DESIGN
The university offers Online Self-paced Learning courses of about 60 hours with 4 credits each. These courses are designed to provide a comprehensive and engaging learning experience, adopting of (4) Four-Quadrant Approach i.e., eContent, eTutorials, Self-Assessments and Discussion Board.
METHODOLOGY
To ensure effective understanding, knowledge retention and practical applications, these courses use the following methodology:
1. Self-paced online course delivered with four-quadrant approach
2. Unit-wise Structured Course with
3. Modular MCQ-based Self-Assessments
4. Assignments for Practical Applications
5. 24x7 Online Community Support from Peers and Faculty
6. Periodic Live Interactive Sessions by Industry Professionals
7. Flexible Course Duration with a minimum of 1 month to a maximum of 1 year.
8. Final Examination (3 hours) after completing 60 hours of learning
a. Early Examination in limited centres near Regional Centres (RCs)
b. Regular Examination twice a year (Semester Pattern) near the students’ location.
NOTE: Not more than 4 (Four) Self-paced Online Courses can be taken concurrently.
Course Objectives
1. Understand the fundamental concepts and theories of Artificial Intelligence (AI)
2. Develop knowledge of various AI techniques and algorithms
3. Learn to apply AI methods to solve real-world problems
4. Understand the ethical implications and societal impact of AI
5. Gain practical experience in implementing AI systems
6. Develop critical thinking skills for evaluating AI solutions
Course Outcomes
By the end of this course, students will be able to:
1. Explain core AI concepts and their applications in various domains
2. Implement basic AI algorithms for problem-solving and decision-making
3. Design and develop intelligent agents using appropriate AI techniques
4. Apply machine learning algorithms to analyze and interpret data
5. Understand and implement basic natural language processing techniques
6. Evaluate the performance and limitations of AI systems
7. Discuss ethical considerations and potential societal impacts of AI
8. Demonstrate familiarity with current trends and future directions in AI research
9. Implement basic computer vision and image processing techniques
10. Apply AI concepts to develop simple game-playing algorithms
.
Definition and history of AI
Types of AI narrow general AI
AI applications and impact on various industries
E-Tutorial 1
Duration:
E-Tutorial 2
Duration:
Assessment
5 Questions
Agent architectures
Search algorithms Uninformed and informed search
Constraint satisfaction problems
E-Tutorial 1
Duration:
E-Tutorial 2
Duration:
Assessment
5 Questions
Propositional and first-order logic
Inference and theorem proving
Probabilistic reasoning and Bayesian networks
E-Tutorial 1
Duration:
E-Tutorial 2
Duration:
Assessment
5 Questions
Supervised learning classification and regression
Unsupervised learning clustering and dimensionality reduction
Reinforcement learning basics
Evaluation metrics and model selection
E-Tutorial 1
Duration:
E-Tutorial 2
Duration:
Assessment
5 Questions
Perceptrons and multilayer networks
Backpropagation and optimization algorithms
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs) and LSTMs
E-Tutorial 1
Duration:
E-Tutorial 2
Duration:
Assessment
5 Questions
Text preprocessing and feature extraction
Syntax and parsing
Sentiment analysis and text classification
Introduction to language models and transformers
E-Tutorial 1
Duration:
E-Tutorial 2
Duration:
Assessment
5 Questions
Image processing fundamentals
Object detection and recognition
Face recognition
Image segmentation
E-Tutorial 1
Duration:
E-Tutorial 2
Duration:
Assessment
5 Questions
Robot kinematics and dynamics
Simultaneous Localization and Mapping (SLAM)
Path planning and navigation
E-Tutorial 1
Duration:
Assessment
5 Questions
Rule-based systems
Fuzzy logic
Ontologies and semantic networks
Assessment
5 Questions
Preparing operating budgets
Cash budgets
Flexible budgets
Forecasting techniques
E-Tutorial 1
Duration:
E-Tutorial 1
Duration:
Assessment
5 Questions
Minimax algorithm
Alpha-beta pruning
Monte Carlo Tree Search
E-Tutorial 1
Duration:
E-Tutorial 2
Duration:
Assessment
5 Questions
Bias and fairness in AI systems
Privacy concerns and data protection
AI governance and regulation
AIs impact on employment and society
Assessment
5 Questions
Explainable AI
AI in edge computing and IoT
Artificial General Intelligence (AGI) concepts
E-Tutorial 1
Duration:
E-Tutorial 2
Duration:
Quantum computing for AI
Assessment
5 Questions
AI project lifecycle
Data collection and preprocessing
Model deployment and maintenance
AI development tools and frameworks
E-Tutorial 1
Duration:
Assessment
5 Questions
Final Assessment
50 Questions
The certificate issued for the Course will have
Only the e-certificate will be made available. No Hard copies. The certificates issued by Odisha State Open University, Sambalpur. can be e-verifiable at www.ulektzskills.com/verify.
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