This course provides an introduction to the theory of computation, including formal models of computation and their relationships to formal languages. Key topics covered include automata, computability, and complexity. Students will learn about regular languages and context-free grammars, as well as limitations of computation like unsolvable problems. The goal is for students to strengthen their mathematical reasoning skills and understand the inherent capabilities and limitations of computers. Assessment includes exams, assignments, and projects. Prerequisites include discrete mathematics.
Introduction to Machine Learning, Supervised Learning, Unsupervised Learning, Algorithms, K-Means, K-Nearest Neighbor, Support Vector Machine, DBSCAN, and Use Cases for each sectors in industries such as finance, e-commerce.
Presentation given by Jan Hanson and Martyn Thomas to the CETL-MSOR Sigma conference, detailing how the sigma funding was used to improve the mathsEG computer aided assessment website/webapp.
Introduction to Machine Learning, Supervised Learning, Unsupervised Learning, Algorithms, K-Means, K-Nearest Neighbor, Support Vector Machine, DBSCAN, and Use Cases for each sectors in industries such as finance, e-commerce.
Presentation given by Jan Hanson and Martyn Thomas to the CETL-MSOR Sigma conference, detailing how the sigma funding was used to improve the mathsEG computer aided assessment website/webapp.
Computer programming is the heart of computing education. It is a fundamental skill that all computing students are required to learn. However, programming courses are generally considered among the most difficult course and often have the highest dropout rates. The main concern is what concerns of computing faculty is to improve students’ motivation in getting involved in meaningful programming activities. This requires special skills to teach programming. A teacher training workshop was arranged at Directorate of Staff Development for newly inducted Computer Science teachers. The workshop covers various approaches proposed in literature for teaching computer programming. Some useful guidelines have also been suggested. These are the slides of this workshop.
Bad Modelling Teaching Practices: Invited talk at MoDELS'14 Educators' Symposiumrichpaige
An invited talk given at MoDELS'14 in Valencia at the Educators' Symposium, focusing on experiences with teaching models and modelling and what things did not work.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Computer programming is the heart of computing education. It is a fundamental skill that all computing students are required to learn. However, programming courses are generally considered among the most difficult course and often have the highest dropout rates. The main concern is what concerns of computing faculty is to improve students’ motivation in getting involved in meaningful programming activities. This requires special skills to teach programming. A teacher training workshop was arranged at Directorate of Staff Development for newly inducted Computer Science teachers. The workshop covers various approaches proposed in literature for teaching computer programming. Some useful guidelines have also been suggested. These are the slides of this workshop.
Bad Modelling Teaching Practices: Invited talk at MoDELS'14 Educators' Symposiumrichpaige
An invited talk given at MoDELS'14 in Valencia at the Educators' Symposium, focusing on experiences with teaching models and modelling and what things did not work.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
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We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
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Course outlin for to c
1. Theory of Computation
Course Description:
Understanding the inherent capabilities and limitations of computers is a fundamental question in
computer science. To answer this question, we will define formal mathematical models of
computation,andstudy theirrelationshipswithformal languages.Topicswill consistof three central
areas of the theory of computation: automata, computability, and complexity.
Course Objectives/StudentLearningOutcomes
StudentsLearningOutcomes. Studentswilllearnseveral formal mathematical modelsof computation
along with their relationships with formal languages. In particular, they will learn regular languages
and context free languages which are crucial to understand how compilers and programming
languages are built. Also students will learn that not all problems are solvable by computers, and
some problems do not admit efficient algorithms. Throughout this course, students will strengthen
their rigorous mathematical reasoning skills.
At the endof thiscourse,studentswillbe able todothe following:
• Studentswill demonstrateknowledge of basicmathematical modelsof computationand
describe how theyrelate toformal languages.
• Studentswill understandthatthere are limitationsonwhatcomputerscando,and learn
examples of unsolvable problems.
• Studentswill learnthatcertainproblemsdonotadmitefficientalgorithms,andidentifysuch
problems.
Program LearningOutcomes. Atthe endof the course,students:
• Will applyknowledge of computingandmathematicsappropriatetothe discipline.
• Will functioneffectivelyasamemberof a team inorderto accomplisha commongoal.
• will applymathematical foundations,algorithmicprinciplesandcomputersciencetheoryto
the modellinganddesignof computerbasedsystemsinawaythat demonstrates
• Will applydesignanddevelopmentprinciplesinthe constructionof softwaresystemsof
varyingcomplexity.
Topics
• Automata and Language: We will studysimplecomputingmodelswhichplayacrucial role in
compilersandprogramming languages.
– Finite automata,regularlanguages,andregulargrammars.
– Contextfree grammars,languages,andpushdownautomata.
– Deterministicandnondeterministicautomata.
• ComputabilityTheory: We will definemore powerful computingmodelstocapture general
computers,andlearnthat notall problemare solvable bycomputers.
2. – Turingmachines,Church’sthesis,andundecidable problems.
• ComplexityTheory:Thistheoryaimsto distinguishdecidable problemsintermsof time and
space complexity.
– Time complexityclassesPandNP.
– ReductionandNP-completeness.
– Space complexity.
A tentative week-by-weekschedule isasfollows.
• Week1. Course overview and basicconceptof Setoperations.
• Week2.Deterministicfinite automata(DFA).
• Week3. Nondeterministicfinite automata(NFA).
• Week4. Equivalence of DFA andNFA,andregularexpressions.
• Week5. Regularexpressionandregularlanguages.
• Week6. Non-regularlanguagesandpumpingLemma,andclosure properties.
• Week7. Optimal DFA and review.
• Week8. Midterm
• Week9. Context-free languages.
• Week10. Pushdownautomataandgrammarsimplification.
• Week11. Chomskynormal formandpumpinglemmaforcontext-freelanguages.
• Week12. Closure propertiesandMembershipTest
• Week13. TuringMachinesand reduction.
• Week14. NP-completeness.
• Week15. Decidabilityand recognisability.
• Week16. More complexitiesandapproximability.
• Week17. Reviewandfinal exam.
Prerequisites
• Discrete Mathematics
Textbooks
No textbookisrequired,butthe followingbookwillbe anexcellent reference.
• Introductiontothe Theoryof ComputationbyMichel Sipser,2ndEd.,Cengage Learning,
2005.
3. • IntroductiontoComputerTheory, Daniel I.A.Cohen, Prentice-Hall,SecondEdition
Allocation of Marks
Assessment Instruments Mark
Midterm examination 20%
Final Term examination 35%
Assignments/ Quizzes 10%
Reports, research projects 25%
Total 100%
Instructor
Ikram Syed,Ph.D.
• Office:GoldCampus,office:18
• Email:ikram.syed@superior.edu.pk
Simulators:
In orderto improve the pedagogyof thiscourse,interactive animationsof the variousautomata
usingavailable simulatorsare recommended.