Artificial Intelligence

Course Title: Artificial Intelligence

Course No: ENCT351

Nature of the Course: Theory + Lab

Semester: 6

Full Marks: 60 + 40 + 50

Pass Marks: 24 + 16 + 20

Credit Hours: 3

Course Description

This course provides students with a foundation in Artificial Intelligence (AI), covering intelligent agents, search techniques, knowledge representation, machine learning, and AI ethics, equipping students with both theoretical understanding and practical skills to apply AI techniques to real-world problems.

Course Objectives

The objective of this course is to:

Provide students with a foundation in Artificial Intelligence (AI), covering intelligent agents, search techniques, knowledge representation, machine learning, and AI ethics
Equip students with both theoretical understanding and practical skills to apply AI techniques to real-world problems
Develop awareness of the ethical and societal implications of AI systems

Course Contents

1. Introduction

1.1. Definition, foundation, history of AI

1.2. Importance of knowledge and learning

1.3. Cognition and learning (Neuroscience)

1.4. Human intelligence and machine intelligence

1.5. AI tree: Branches and interdisciplinary nature

1.6. Intelligent agents and types

1.7. Agentic AI

2. Problem Solving and Search

9 hrs12 marks

2.1. Formal problem definition: States, actions, transitions, well-defined problems

2.2. Constraint satisfaction problems: Node consistency, path consistency, backtracking

2.3. Search algorithms, strategies and evaluations

2.4. Uninformed: BFS, DFS, iterative deepening

2.5. Informed Search: Best first search, greedy search, A* algorithm

2.6. Adversarial search: Minimax algorithm, alpha-beta pruning

2.7. Local search and optimization: Hill climbing, simulated annealing

2.8. Evolutionary optimization: Genetic algorithm

3. Knowledge Representation and Probabilistic Reasoning

7 hrs10 marks

3.1. Knowledge-based agent

3.2. Knowledge representation techniques and issues in representation

3.3. Propositional and predicate logic

3.4. Semantic networks, frames and knowledge graph

3.5. Review of Bayes' Theorem and probabilistic reasoning

3.6. Fuzzy logic: Membership functions, fuzzy inference systems

4. Machine Learning Fundamentals

10 hrs14 marks

4.1. Foundations and four pillars of machine learning

4.2. Review of mathematics for machine learning

4.3. Continuation optimization; Unconstraint optimization, constraint optimization, convex optimization

4.4. Review of machine learning pipeline

Model development: Generative versus discriminative

Learning algorithm

Capacity, overfitting and underfitting

Hyperparameters and validation sets

Estimators, bias and variance

Review of MLE and MAP and cross entropy

4.5. Supervised learning algorithm: Decision tree and support vector machine

4.6. Unsupervised learning algorithm: t-SNE

4.7. Semi supervised and reinforcement learning

4.8. Model evaluation: Energy-based indicators

5. Neural Networks and Deep Learning Algorithms

8 hrs10 marks

5.1. Neural networks: Structures, activation functions and universal approximation theorem

5.2. Perceptron, multilayer perceptron and backpropagation

5.3. Introduction to deep learning

5.4. Concepts on recurrent and generative neural networks

6. AI Applications

6.1. Expert systems: Characteristics, architecture, development and various applications

6.2. NLP: Level of analysis, challenges, modern approaches and applications

6.3. Robotics and computer vision: Fundamental, components and applications

6.4. Sustainable AI systems

7. Emerging Trends

7.1. Sequence to sequence models

7.2. Federated learning

7.3. Edge AI

7.4. Ethics and AI: Responsible AI

Laboratory Works

Practical sessions cover intelligent agents and problem formulation, uninformed and informed search techniques, adversarial search, constraint satisfaction problems, evolutionary computation, knowledge representation, probabilistic and fuzzy reasoning, machine learning pipeline, supervised and unsupervised learning, neural networks and deep learning, and AI applications and ethics. (45 hours)

1.Intelligent Agents and Problem Formulation
2.Uninformed Search Techniques
3.Informed (Heuristic) Search Techniques
4.Adversarial Search and Game Playing
5.Constraint Satisfaction Problems (CSP)
6.Evolutionary Computation (Genetic Algorithms)
7.Knowledge Representation (Logic, Semantic Networks, Frames)
8.Probabilistic and Fuzzy Reasoning
9.Machine Learning Pipeline and Data Preprocessing
10.Supervised and Unsupervised Learning
11.Neural Networks and Deep Learning Basics
12.Mini Project, AI Applications and Ethics

Reference Books

1.Russell, S., Norvig, P. (2021). Artificial intelligence: A modern approach. Pearson.
2.Rich, E., Knight, K., Nair, S. B. (2009). Artificial intelligence. McGraw-Hill Education.
3.Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
4.Deisenroth, M. P., Faisal, A. A., Ong, C. S. (2020). Mathematics for machine learning. Cambridge University Press.
5.Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep learning. MIT Press.

Notes:

This syllabus follows the official BCT curriculum of Tribhuwan University. In case of any doubt or revision, the university's published syllabus shall be considered authoritative. https://ioe.tu.edu.np/pages/computer-engineering-curriculum-structure-2635

Note: Chapters 1, 6, and 7 together carry 14 marks and require approximately 11 hours of study.

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