Course Syllabus

JOHN CABOT UNIVERSITY

COURSE CODE: "CS 340 - 1"
COURSE NAME: "Intelligent Systems"
SEMESTER & YEAR: Spring 2026

SYLLABUS

INSTRUCTOR: Marco Pascucci
EMAIL: [email protected]
HOURS: TTH 1:30 PM 2:45 PM
TOTAL NO. OF CONTACT HOURS: 45
CREDITS: 3
PREREQUISITES: Pre-requisites: CS 160 and one of CS 200 or MA 208. Recommended: CS 330
OFFICE HOURS: by appointment (Tue, Thu)

COURSE DESCRIPTION:

This course introduces students to the theory and practice of intelligent systems. It covers both historical and modern approaches to Artificial Intelligence, with hands-on experience in coding intelligent behaviours and integrating AI models into software projects. Special emphasis is placed on critical thinking, creative applications, and the effective use of modern AI-assisted coding tools.

SUMMARY OF COURSE CONTENT:

Intelligent Systems is an upper-level course that introduces students to the foundations and applications of Artificial Intelligence (AI). The course begins with a historical overview of the field, from early symbolic reasoning systems and expert systems to modern machine learning and deep learning approaches. Students will study both classical methods that do not rely on machine learning, such as search, rule-based reasoning, and planning, as well as contemporary data-driven models.

A strong hands-on component allows students to design and implement intelligent behaviors, train and evaluate machine learning models, and integrate large-scale AI services into applications via API. The course also emphasizes coding with AI assistants, preparing students to use modern AI-powered development tools effectively and critically. Alongside technical practice, students will reflect on the broader cultural, ethical, and creative dimensions of AI, considering its role in shaping knowledge, creativity, and society.

By the end of the course, students will have both a conceptual understanding of intelligent systems and practical experience in building AI-enhanced software projects, equipping them with skills valuable across disciplines.

TOPICS DETAIL

Introduction to AI

o Definitions of intelligence and intelligent systems

o Brief history of Artificial Intelligence: symbolic AI, expert systems, machine learning, and large models

Coding with AI Assistants

o Using AI-powered tools for programming support

o Prompting strategies, verification, and debugging

o Best practices and ethical considerations in AI-assisted coding

Classical AI (Non-ML Approaches)

o Search and planning

o Rule-based systems and expert systems

o Knowledge representation and logic

o Game Playing Strategies

Machine Learning and Deep Learning

o Supervised, unsupervised, and reinforcement learning

o Neural networks, deep learning, and modern architectures (transformers)

o Applications and limitations of ML/DL

Large AI Models in Applications

o Overview of large language models and generative AI

o Accessing AI services via APIs (e.g., OpenAI)

o Building simple applications that integrate LLMs or other AI models

Ethics and Critical Perspectives

o Social, cultural, and creative implications of AI

o Bias, transparency, and responsible AI use

Hands-On Projects and Applications

o In-class coding exercises aligned with each topic

o Possible interdisciplinary mini-projects

o Final project: design and present an AI-enabled software application

Note: The summary of course content provided above is a preliminary draft and is subject to revision as the course undergoes further development and review.

LEARNING OUTCOMES:

Conceptual Understanding
Students will be able to explain key concepts in Artificial Intelligence, from non-statistical models to machine learning, and situate these within the historical and contemporary development of the field of Computer Science.

Practical Application
Students will be able to design and implement intelligent behaviors and integrate AI services into software projects. They will learn effective use of AI-assisted coding tools in software development.

Critical Reflection
Students will have a basic critical understanding of the benefits and limitations of modern AI tools, as well as ethical, social, and creative implications of AI technologies.

TEXTBOOK:

NONE

REQUIRED RESERVED READING:

NONE

RECOMMENDED RESERVED READING:

NONE

GRADING POLICY
-ASSESSMENT METHODS:

Assignment	Guidelines	Weight
Presence and participation in in-class exercises		20%
Midterm assignment		20%
Final project		60%

-ASSESSMENT CRITERIA:
AWork of this quality directly addresses the question or problem raised and provides a coherent argument displaying an extensive knowledge of relevant information or content. This type of work demonstrates the ability to critically evaluate concepts and theory and has an element of novelty and originality. There is clear evidence of a significant amount of reading beyond that required for the course.
BThis is highly competent level of performance and directly addresses the question or problem raised.There is a demonstration of some ability to critically evaluatetheory and concepts and relate them to practice. Discussions reflect the student’s own arguments and are not simply a repetition of standard lecture andreference material. The work does not suffer from any major errors or omissions and provides evidence of reading beyond the required assignments.
CThis is an acceptable level of performance and provides answers that are clear but limited, reflecting the information offered in the lectures and reference readings.
DThis level of performances demonstrates that the student lacks a coherent grasp of the material.Important information is omitted and irrelevant points included.In effect, the student has barely done enough to persuade the instructor that s/he should not fail.
FThis work fails to show any knowledge or understanding of the issues raised in the question. Most of the material in the answer is irrelevant.

-ATTENDANCE REQUIREMENTS:
ATTENDANCE REQUIREMENTS AND EXAMINATION POLICY
You cannot make-up a major exam (midterm or final) without the permission of the Dean’s Office. The Dean’s Office will grant such permission only when the absence was caused by a serious impediment, such as a documented illness, hospitalization or death in the immediate family (in which you must attend the funeral) or other situations of similar gravity. Absences due to other meaningful conflicts, such as job interviews, family celebrations, travel difficulties, student misunderstandings or personal convenience, will not be excused. Students who will be absent from a major exam must notify the Dean’s Office prior to that exam. Absences from class due to the observance of a religious holiday will normally be excused. Individual students who will have to miss class to observe a religious holiday should notify the instructor by the end of the Add/Drop period to make prior arrangements for making up any work that will be missed. The final exam period runs until ____________

ACADEMIC HONESTY
As stated in the university catalog, any student who commits an act of academic dishonesty will receive a failing grade on the work in which the dishonesty occurred. In addition, acts of academic dishonesty, irrespective of the weight of the assignment, may result in the student receiving a failing grade in the course. Instances of academic dishonesty will be reported to the Dean of Academic Affairs. A student who is reported twice for academic dishonesty is subject to summary dismissal from the University. In such a case, the Academic Council will then make a recommendation to the President, who will make the final decision.

STUDENTS WITH LEARNING OR OTHER DISABILITIES
John Cabot University does not discriminate on the basis of disability or handicap. Students with approved accommodations must inform their professors at the beginning of the term. Please see the website for the complete policy.

SCHEDULE

Week 1	Introduction to AI
Week 2	Coding with AI Assistants
Week 3	Intro to Classical AI (Non-ML Approaches)
Week 4	Knowledge representation
Week 5	Review	Midterm
Week 6	Introduction to Machine Learning
Week 7	Deep Learning Basics
Week 8	Advanced ML & Applications
Week 9	Large Models & APIs
Week 10	AI in Applications
Week 11	Ethics, Society & Creativity
Week 12	Review
Week 13	Work on Final Projects
Week 14	Presentations and Wrap-up	Presentation