CSC 309: Artificial Intelligence
CSC 309 - Artificial Intelligence (3 Units)
Instructor: Oladimeji Kazeem
Last Updated: 04-11-2026 | Language: English
What you'll learn
- ✔ The Foundations and Philosophy of AI
- ✔ Building and Evaluating Intelligent Agents
- ✔ Problem-Solving through Searching Algorithms
- ✔ Knowledge Representation and Artificial Reasoning
- ✔ Language, Vision, and Industry Applications
- ✔ Hands-On Practical Projects and Collaboration
Who this course is for
- Undergraduate students enrolled in B.Sc. Computer Science and B.Sc. Cybersecurity across Nigerian universities.
- 300 Level (Year 3) undergraduate students fulfilling core computing and intelligence systems requirements.
Seminar Class Schedule
4th December 2026 to 30th January 2027
Wed - 12:00 PM WAT | Thu - 2:00 PM WAT | Sat - 3:00 PM WAT
All times are in West African Time (WAT, UTC+1).
Lecture dates (7 lectures)
-
Dec
02
Wed
Module 1: Foundations, Evolution, and the Philosophy of AI Next
Wednesday, 2nd December 2026 12:00 PM – 2:00 PM WAT Zoom
-
Dec
09
Wed
Module 2: Intelligent Agents & Problem Formulation
Wednesday, 9th December 2026 12:00 PM – 2:00 PM WAT Zoom
-
Dec
16
Wed
Module 3: Classical & Uninformed Search Strategies
Wednesday, 16th December 2026 12:00 PM – 2:00 PM WAT Zoom
-
Dec
23
Wed
Module 4: Heuristic Search & Adversarial Methods
Wednesday, 23rd December 2026 12:00 PM – 2:00 PM WAT Zoom
-
Dec
30
Wed
Module 5: Knowledge Representation and Logical Inference
Wednesday, 30th December 2026 12:00 PM – 2:00 PM WAT Zoom
-
Jan
06
Wed
Module 6: Natural Language Processing & Expert Systems
Wednesday, 6th January 2027 12:00 PM – 2:00 PM WAT Zoom
-
Jan
13
Wed
Module 7: Computer Vision, AI Tooling & Project Defense
Wednesday, 13th January 2027 12:00 PM – 2:00 PM WAT Zoom
Course Content
Description
CSC 309 introduces the fundamental concepts, theories, and algorithmic foundations of Artificial Intelligence (AI). The course covers the historical development, philosophy, and ethical implications of synthetic cognition, contrasting rational agency with human-like behavioural models. Students explore the formal design and taxonomy of intelligent agents, their operational environments, and core problem-solving methodologies grounded in state-space search - encompassing uninformed, heuristic, and adversarial strategies under combinatorial constraints.
The curriculum provides rigorous grounding in knowledge representation (KR) and computational reasoning, focusing on First-Order Predicate Logic (FOPL), Skolemization, Conjunctive Normal Form (CNF) conversion, and automated theorem proving via resolution refutation, alongside structured schemata such as semantic networks and frames. Students are introduced to the core mechanics of Natural Language Processing (syntactic parsing and semantic extraction), Expert System architectures (forward/backward chaining), and basic digital image perception and recognition. Applied laboratory sessions reinforce theoretical principles through programming implementations, state-space simulations, agent modelling, and practical deployments of NLP and computer vision pipelines using standard AI tooling.
Course Learning Outcomes (CLOs)
Upon successful completion of this course, students will be able to:
- Synthesise AI Fundamentals & History:
- Explain foundational concepts, historical milestones, philosophical debates (including the Turing Test and Searle's Chinese Room argument), system goals, core branches, societal impacts, and modern AI development frameworks.
- Formulate & Evaluate Intelligent Agents:
- Characterise problem environments using the PEAS (Performance, Environment, Actuators, Sensors) framework, classify environment properties, and design appropriate agent architectures (reflex, model-based, goal-based, and utility-based).
- Analyse & Implement State-Space Search Algorithms:
- Formulate real-world problems into state-space representations, evaluate the impact of combinatorial explosion, and analyse the trade-offs between completeness, optimality, time complexity, and space complexity across blind (BFS, DFS, IDS, UCS) and informed ($A^*$, Greedy Best-First) search strategies.
- Construct Formal Knowledge Representations & Automated Inferences:
- Translate natural language specifications into first-order predicate logic, convert logical formulas into Conjunctive Normal Form (clause form) via standard reduction steps and Skolemisation, and execute resolution-refutation algorithms to prove theorems or answer domain queries.
- Utilise Structured Knowledge & Expert Systems:
- Design knowledge structures using semantic networks and frame systems with property inheritance, and analyse the mechanics of rule-based expert systems, differentiating between data-driven (forward) and goal-directed (backward) inference engines.
- Implement Core Natural Language & Vision Pipelines:
- Demonstrate practical competence in text analysis workflows (tokenisation, POS tagging, classification, sentiment evaluation, and rule-based conversational interfaces) and basic digital image operations (matrix filtering and spatial feature extraction).
- Develop and deploy applied AI solutions:
- Collaborate in technical teams to design, program, debug, and defend functional AI software - such as multi-agent simulations, text-processing engines, or autonomous search agents - using industry-standard programming environments (Python).