Course lectures and notes.
Lecture slides and notes for current courses at CASPAM. Choose a course to see its lectures; new material is added through the semester.
Fall 2026 teaching schedule
9 weekly sessions · with effect from 24-08-2026. Open the full CASPAM timetable ↗
| Day | Time | Course | Class | Room |
|---|---|---|---|---|
| Monday | 10:00 AM – 12:00 PM | Fundamentals of Machine Learning MATH 461 | BS Mathematics Morning & Evening, Semester 7 | CC Hall |
| Tuesday | 11:30 AM – 1:30 PM | Database Management Systems (DBMS) COMP 351 | BS Mathematics Morning, Semester 5 Post ADP Mathematics Morning, Semester 1 | Room 206 |
| Wednesday | 10:00 AM – 11:30 AM | Discrete Structures GE 105 | BS(AI) Morning, Semester 1 | Math-106 |
| 11:30 AM – 1:30 PM | Database Management Systems (DBMS) COMP 351 | BS Mathematics Morning, Semester 5 Post ADP Mathematics Morning, Semester 1 | C Lab | |
| Thursday | 10:00 AM – 11:30 AM | Discrete Structures GE 105 | BS(AI) Morning, Semester 1 | Math-106 |
| 12:00 PM – 2:00 PM | Artificial Intelligence CS 205 | BS(AI) Evening, Semester 3 | Math-202 | |
| Friday | 8:00 AM – 10:00 AM | Civic and Community Engagement SOCL 201 | BS Mathematics Morning, Semester 3 | Room 206 |
| 8:30 AM – 11:30 AM | Artificial Intelligence CS 205 | BS(AI) Evening, Semester 3 | Math-202 | |
| 12:00 PM – 1:30 PM | Fundamentals of Machine Learning MATH 461 | BS Mathematics Morning & Evening, Semester 7 | CC Hall |
Artificial Intelligence
Foundations of AI: intelligent agents, problem formulation, search, knowledge representation, reasoning and knowledge-based systems, with Python implementations.
Learning outcomes
- Formulate problems as state spaces and solve them with uninformed and informed search
- Represent knowledge in logic and rules, and perform inference
- Build a small knowledge-based or search-driven decision system
- L01
Introduction to Artificial Intelligence & Knowledge-Based Systems
Foundations of AI, knowledge-based systems, symbolic manipulation, pattern matching, decision making, and the relationship among knowledge, data and code.
Coming soon - L02
Reasoning and Knowledge Representation
Representation of knowledge, inference, rules and symbolic approaches for intelligent systems.
Coming soon
Machine Learning
From the mathematics of learning (linear algebra, probability and optimisation) to building, evaluating and explaining models in Python and scikit-learn.
Learning outcomes
- Explain loss, bias, variance and generalisation mathematically
- Train and validate supervised models on real datasets
- Evaluate and communicate model performance with appropriate metrics
- Lectures for this course will appear here as they are published.
Database Systems (DBMS)
Relational model, relational algebra, SQL, ER modelling, normalisation and transactions, leading to a working module of a university or business ERP in PostgreSQL.
Learning outcomes
- Model a domain with entities, relationships and keys
- Write relational algebra and SQL queries
- Design and normalise a PostgreSQL schema for a real application
- L01
Relational Algebra, SQL and Database Foundations
Relational models, relational algebra, SQL, schemas, keys and database foundations.
Coming soon
Discrete Structures
Sets, relations, functions, logic, proof, combinatorics and graphs, extended through systems thinking and system design and modelling towards product modelling.
Learning outcomes
- Reason precisely with sets, relations, functions and proofs
- Use counting and graph models to analyse discrete systems
- Apply discrete models to system design and product modelling
- L01
Sets, Relations and Functions
Core discrete structures including sets, relations, equivalence, orders, functions, composition and applications.
Coming soon
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