PU(Maubin), Faculty of Computing's LMS: All courses

Course categories:

CS-4214 Advanced Artificial Intelligence

Course Objectives

To provide the most fundamental concept of knowledge representation and reasoning in Artificial Intelligence.
To provide the ability to analyze and inference upon Uncertain Knowledge conditions
To understand the basic theory underlying machine learning.
To gain practical experience through coding exercises, projects, and assignments involving datasets.

Course Description

This course is intended to introduce the basic principles, techniques, and applications of the field of artificial intelligence: Knowledge representation and Uncertain Knowledge and reasoning algorithms. This course provides an introduction to the fundamental concepts and techniques of machine learning. Machine learning is a critical field within artificial intelligence that enables computers to learn from data and make intelligent decisions. Through a combination of theoretical lectures and hands-on practical sessions, students will gain a strong understanding of the principles and methodologies that underlie various machine learning algorithms.

Reference Book

Artificial Intelligence- A Modern Approach (Third Edition) by Stuart Russell & Peter Norvig
Ronald J. Brachman and Hector J. Levesque, Knowledge Representation and Reasoning, Elsevier/Morgan Kaufmann 2004. ISBN 1-55860-932-6.
Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Müller and Sarah Guido

Learning Outcomes

Have knowledge and understanding of the basic concepts of knowledge representation and reasoning.
Develop a solid understanding of the fundamental concepts, terminology, and principles of machine learning.
Grasp the difference between supervised, unsupervised, and reinforcement learning.

Course Organization