CS-5103 Mathematics for Artificial Intelligence
MS (Artificial Intelligence)
SPRING-24
Department of Computer and Information Systems Engineering
NED University of Engineering and Technology
Instructor: Dr. Muhammad Saleheen Aftab
Class Schedule: Thursday, 6:00pm to 9:00pm
Course Objective:
This course aims to equip students with a strong foundation in the mathematics essential for
understanding and developing Artificial Intelligence systems. By the end of the course,
students will be able to:
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Apply logic frameworks for representing knowledge and reasoning in AI.
Manipulate and analyze data using advanced techniques from linear algebra, multivariate calculus, and probability.
Optimize and analyze algorithms used in machine learning and other AI applications.
Understand and utilize computational methods for implementing AI models.
Develop a strong grasp of the mathematical foundations underlying various AI
techniques.
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Recommended Books:
The following reference books will be used for different topics throughout the semester:
1. Rosen, Kenneth H. Discrete mathematics and its applications. The McGraw Hill
Companies, 2007.
2. Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for
machine learning. Cambridge University Press, 2020.
3. Bender, Edward A. Mathematical methods in artificial intelligence. 1996.
Assessment and Grading Policy:
Sessional Marks
Final Exam
Assignments
Quizzes
Mid Term Exam
Final Exam
10%
10%
20%
60%
5, 8, 12
Weeks 4, 7, 12, 14
Week 9
Week 16/17
Weekly Lecture Breakdown:
Week
1
2
3
4
5
6
7
Topics to be covered
Propositional Logic
Logical Inference; Introduction to First-Order Logic
Nested Quantifiers; Argument building
Linear Algebra for AI
Dimensionality Reduction: Singular Value Decomposition (SVD)
Dimensionality Reduction: Principle Component Analysis (PCA)
Probabilistic and Bayesian Reasoning
8
9
10
11
12
13
14
15
Probabilistic and Bayesian Reasoning
Mid Term Examination
Hidden Markov Models
Maximum A Posteriori (MAP) and Maximum Likelihood Estimation (MLE)
Optimization Theory
Optimization Theory
Linear Regression and Classification Methods
Linear Regression and Classification Methods