facebook
favorite button
super instructor icon
Trusted teacher
This teacher has a fast response time and rate, demonstrating a high quality of service to their students.
member since icon
Since June 2018
Instructor since June 2018
repeat students icon
9 repeat students
Trusted choice for 9 returning students
Systematic Reasoning and Logical Thinking for Computer Science
course price icon
From 47.96 C$ /h
arrow icon
You will learn Systematic Reasoning & Logical Thinking which is a requirement for entering Computer Science program in many universities.
The book “Delftse Foundations of Computation” especially its second chapter will be the main source of our lesson, but other more in-depth books will be also covered if you want to improve even further on logical thinking.
The topics in our lesson include:
• Propositional Logic: Logical operators; Precedence rules; Logical equivalence; Implications in English; Exclusive or; Universal operators; Classifying propositions
• Boolean Algebra: Substitution laws
• Logic Circuits: Logic gates; Combining gates to create circuits; From circuits to propositions; Disjunctive Normal Form; Binary addition.
• Predicate Logic: Predicates; Quantifiers; Tarski’s world and formal structures;
• Deduction: Valid arguments and proofs; Proofs in predicate logic

If you have any additional questions before starting a class, please feel free to ask me. I am here to assist! :)
Location
location type icon
Online from Netherlands
About Me
I am currently a postdoctoral researcher at Delft University of Technology

I like sharing my knowledge with others. I believe that teaching makes the knowledge I have acquired as a researcher much more valuable. In my idea, successful teachers have several qualities including knowledge, enthusiasm, and student-based teaching methods.
Education
PhD: Mechanical Engineering, Applied Design, 2016
Masters degree: Mechanical Engineering, Applied Design, 2011
Bachelors degree: Mechanical Engineering, Solid mechanics, 2008
Experience / Qualifications
My university teaching experience:
- Lecturer of Mechanical Engineering Design I, Materials Science, Dynamics, Engineering
Mathematics, Engineering Drawing I and II, Mechanics of Materials I and II
- Teaching assistant of Statics, Linear vibration, and Engineering drawing
Age
Children (7-12 years old)
Teenagers (13-17 years old)
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Intermediate
Advanced
Duration
45 minutes
60 minutes
90 minutes
120 minutes
The class is taught in
English
Persian
Reviews
Availability of a typical week
(GMT -04:00)
New York
at teacher icon
Online via webcam
Mon
Tue
Wed
Thu
Fri
Sat
Sun
00-04
04-08
08-12
12-16
16-20
20-24
I hold a PhD in Mechanical Engineering and currently work as a post-doctoral researcher in the Aerospace Engineering Faculty at TU Delft (Delft University of Technology).
Key topics that I teach for this course include:
• Calculus: Differentiation & integration, Multivariable calculus
• Linear Algebra: Matrices, vectors, systems of equations, Eigenvalues and eigenvectors
• Differential Equations: First- and second-order ODEs, Laplace transforms
• Complex Numbers: Polar form, Euler’s formula
• Vector Calculus: Gradient, divergence, curl, Line and surface integrals
• Series & Transforms: Taylor series, Fourier series

I understand that not all teachers excel in teaching Engineering and Mathematics courses and explaining their importance to students. Courses with strong mathematical backgrounds may become boring for students who lack a solid mathematical basis. To address this, I take a student-centered approach, starting from the basics to actively engage all students, including those with weaker mathematical backgrounds. I also provide practical examples to demonstrate the real-world significance of what they are learning, inspiring and fostering their interest in the subject matter.

If you have any additional questions before starting a class, please feel free to ask me. I am here to assist! :)
Read more
I hold a PhD in Mechanical Engineering and currently, I am working as a post-doctoral researcher in the Aerospace Engineering Faculty at TU Delft (Delft University of Technology).

I have extensive experience in teaching various Mechanical design packages, including LS-DYNA, ANSYS, APDL programming, ABAQUS, MATLAB, SolidWorks, Cura, and Symbolic MATLAB.

Some of the finite element modeling and numerical simulations I have worked on include:
- Multiscale finite element modeling for fatigue crack propagation in porous materials.
- Analysis of multiple-layer sandwich structures' resistance to bird-strike impact [Read paper].
- Investigation of debonding propagation in aluminum and glass/epoxy composite joints under fatigue loading using Fracture Mechanics principles.
- Computational prediction of the fatigue behavior of additively manufactured porous metallic biomaterials.
- Study of the effect of material type, stacking sequence, and impact location on pedestrian head injury in collisions.

If you have any additional questions before starting the class, please don't hesitate to ask me. I am here to help! :)
Read more
Show more
arrow icon
Similar classes
arrow icon previousarrow icon next
verified badge
Vincent
With over seven years of experience in teaching Computer Science & Information Technology (ICT), I have developed a strong expertise in delivering high-quality education across multiple internationally recognized curricula, including Cambridge IGCSE, GCSE, A-Levels, O-Levels, and Checkpoint. My passion lies in equipping students with coding, cybersecurity, and digital literacy skills, ensuring they are well-prepared for the evolving demands of the digital world.

Expertise & Teaching Areas:
✅ Programming & Software Development: Python, Java, C++
✅ Cybersecurity: Ethical hacking, data protection, network security
✅ Digital Literacy: ICT applications, online safety, cloud computing
✅ Data Science & AI: Data analysis, machine learning fundamentals
✅ Web Development: HTML, CSS, JavaScript

Curriculum & Pedagogical Experience:
🔹 Cambridge IGCSE & GCSE ICT & Computer Science – Teaching core and extended syllabi, focusing on programming logic, databases, and networking.
🔹 Cambridge A-Levels & O-Levels Computer Science – Preparing students for advanced computing concepts, problem-solving, and algorithm development.
🔹 Cambridge Checkpoint ICT – Building foundational skills in digital technology and computer applications.

Professional Impact:
📌 Mentored students to achieve top grades in Cambridge ICT & Computer Science exams.
📌 Developed interactive lesson plans integrating real-world applications of technology.
📌 Conducted coding boot camps and cybersecurity workshops to enhance practical learning.
📌 Guided students in project-based learning, including app development and website design.

With a strong commitment to student-centered learning and technological innovation, I am dedicated to shaping future tech leaders and empowering learners with skills relevant to careers in technology, data science, and software development.
verified badge
Raouf
Objective: To understand AI without fear, to use it to simplify one's life, to know how to identify digital traps, and to use Word, Excel, etc. without difficulty.

1: Demystifying AI (What exactly is it?)
AI is not a movie robot: Difference between fiction and reality.

How it works (simply): The image of the "giant library": AI has read billions of books and uses them to predict the continuation of a sentence or create an image.

Where is it already present? Spell checkers, Netflix/YouTube suggestions, GPS, and voice assistants (Siri/Alexa).

2: Using AI to make life easier
Conversing with AI (ChatGPT, Claude, Gemini):

Ask him to write an administrative email or a complex letter.

Summarize a long newspaper article or document.

Plan a travel itinerary or find recipe ideas with what's left in the fridge.

AI for creativity and memory:

Generate images to illustrate a birthday card (Midjourney, DALL-E).

Using AI to restore or colorize old family photos.

3: Learning to "talk" to AI (The Art of the Prompt)
The context method: Why "Give me a cake recipe" is less effective than "I am allergic to gluten and I am hosting 4 people, give me a simple chocolate cake recipe".

The expert's role: Learning to tell AI "Act like a travel guide" or "Act like an expert gardener".

4: Precautions and Critical Thinking (The Survival Guide)
"Hallucinations": Understand that AI can make false claims with complete certainty (never take medical or legal advice from AI without verification).

Privacy protection:

Never give sensitive data (social security number, passwords, bank details) to an AI.

Knowing that everything we write to the AI is potentially used to train it.

Spotting "Deepfakes":

How to recognize a doctored image or video (details on the hands, strange reflections, slightly metallic voice).

Verify the information: the golden rule of cross-referencing sources.

5: Ethics and Impacts (To go further)
Copyright: Who owns an image created by AI?

The environmental impact: The water and energy consumption of AI servers.

The future: Will AI replace us or assist us?
verified badge
Ammar
Artificial Intelligence and programming become much easier when you understand the reasoning behind the algorithms—not simply memorize Python syntax or use AI tools as a black box.

I am a PhD-qualified engineer, university professor, researcher, programmer, and multidisciplinary tutor with more than 30 years of experience across teaching, technical training, engineering, information systems, quantitative analysis, research, data mining, programming, and intelligent knowledge-based systems.

This class provides a structured and personalized pathway for beginners, school and university students, researchers, engineers, professionals, career changers, and adult learners. Depending on your goals, we can focus on Python programming, computational problem solving, automation, machine learning, artificial intelligence, or a coherent progression connecting them.

PYTHON PROGRAMMING & COMPUTATIONAL THINKING
• Python installation and development environments
• Variables, data types, operators, and expressions
• Input, output, and program flow
• Conditional statements and decision making
• For loops and while loops
• Functions, parameters, return values, and scope
• Strings and text processing
• Lists, tuples, sets, and dictionaries
• File handling and data input/output
• Error handling and exceptions
• Modules, packages, and reusable code
• Object-oriented programming
• Algorithms and computational problem solving
• Debugging and systematic error correction
• Code organization, readability, and good programming practices

SCIENTIFIC COMPUTING, DATA & AUTOMATION
• NumPy for numerical computing
• pandas for structured data manipulation
• matplotlib for visualization
• Scientific and engineering calculations
• Automation of repetitive tasks
• Data processing workflows
• Working with files and external data
• Introduction to APIs when relevant
• Python and SQL workflows
• Research and quantitative applications
• Project development from idea to working solution

MACHINE LEARNING
• Foundations of machine learning
• Supervised and unsupervised learning
• Regression and classification
• Clustering and pattern discovery
• Decision trees and rule-based approaches
• Feature selection and data preparation
• Training, validation, and testing
• Model evaluation and performance metrics
• Overfitting and underfitting
• Bias, variance, and generalization
• Model comparison and interpretation
• Predictive modelling and data mining
• Neural-network foundations

ARTIFICIAL INTELLIGENCE & INTELLIGENT SYSTEMS
• Foundations and major branches of Artificial Intelligence
• How intelligent systems represent, classify, predict, and support decisions
• Knowledge representation concepts
• Ontologies and structured knowledge
• Rule-based reasoning and expert-system foundations
• Intelligent decision-support systems
• Generative AI and large language model concepts
• Prompt design and effective AI-assisted workflows
• AI limitations and hallucinations
• Bias, privacy, ethics, and responsible AI
• Applications in engineering, research, business, education, and professional decision making

Depending on your goals, practical work may involve Python, NumPy, pandas, matplotlib, relevant machine-learning libraries, Weka, SPSS Modeler, structured datasets, or modern generative-AI tools.

My teaching approach follows a clear progression:
understand the problem → design the logic → represent and prepare the data → write or select the method → test it → evaluate the output → debug or improve it → interpret the result → apply it responsibly

I do not simply provide finished code, demonstrate isolated commands, or recommend an AI model because it is popular. I help you understand why a method works, what assumptions it makes, when it should be used, how to evaluate its results, where it may fail, and how to improve the solution.

We can work with your course syllabus, programming exercises, existing code, error messages, dataset, research problem, AI project, automation task, engineering application, model output, or professional use case.

Whether you are writing your first Python program, preparing for a university course, debugging a project, automating a professional task, learning machine learning, or exploring advanced AI applications, I will adapt the sessions to your level, objectives, and pace.

My goal is to help you become an independent computational problem solver who can understand, build, evaluate, and apply intelligent solutions—not merely copy code or use AI tools without understanding them.
message icon
Contact Reza
repeat students icon
1st lesson is backed
by our
Good-fit Instructor Guarantee
Similar classes
arrow icon previousarrow icon next
verified badge
Vincent
With over seven years of experience in teaching Computer Science & Information Technology (ICT), I have developed a strong expertise in delivering high-quality education across multiple internationally recognized curricula, including Cambridge IGCSE, GCSE, A-Levels, O-Levels, and Checkpoint. My passion lies in equipping students with coding, cybersecurity, and digital literacy skills, ensuring they are well-prepared for the evolving demands of the digital world.

Expertise & Teaching Areas:
✅ Programming & Software Development: Python, Java, C++
✅ Cybersecurity: Ethical hacking, data protection, network security
✅ Digital Literacy: ICT applications, online safety, cloud computing
✅ Data Science & AI: Data analysis, machine learning fundamentals
✅ Web Development: HTML, CSS, JavaScript

Curriculum & Pedagogical Experience:
🔹 Cambridge IGCSE & GCSE ICT & Computer Science – Teaching core and extended syllabi, focusing on programming logic, databases, and networking.
🔹 Cambridge A-Levels & O-Levels Computer Science – Preparing students for advanced computing concepts, problem-solving, and algorithm development.
🔹 Cambridge Checkpoint ICT – Building foundational skills in digital technology and computer applications.

Professional Impact:
📌 Mentored students to achieve top grades in Cambridge ICT & Computer Science exams.
📌 Developed interactive lesson plans integrating real-world applications of technology.
📌 Conducted coding boot camps and cybersecurity workshops to enhance practical learning.
📌 Guided students in project-based learning, including app development and website design.

With a strong commitment to student-centered learning and technological innovation, I am dedicated to shaping future tech leaders and empowering learners with skills relevant to careers in technology, data science, and software development.
verified badge
Raouf
Objective: To understand AI without fear, to use it to simplify one's life, to know how to identify digital traps, and to use Word, Excel, etc. without difficulty.

1: Demystifying AI (What exactly is it?)
AI is not a movie robot: Difference between fiction and reality.

How it works (simply): The image of the "giant library": AI has read billions of books and uses them to predict the continuation of a sentence or create an image.

Where is it already present? Spell checkers, Netflix/YouTube suggestions, GPS, and voice assistants (Siri/Alexa).

2: Using AI to make life easier
Conversing with AI (ChatGPT, Claude, Gemini):

Ask him to write an administrative email or a complex letter.

Summarize a long newspaper article or document.

Plan a travel itinerary or find recipe ideas with what's left in the fridge.

AI for creativity and memory:

Generate images to illustrate a birthday card (Midjourney, DALL-E).

Using AI to restore or colorize old family photos.

3: Learning to "talk" to AI (The Art of the Prompt)
The context method: Why "Give me a cake recipe" is less effective than "I am allergic to gluten and I am hosting 4 people, give me a simple chocolate cake recipe".

The expert's role: Learning to tell AI "Act like a travel guide" or "Act like an expert gardener".

4: Precautions and Critical Thinking (The Survival Guide)
"Hallucinations": Understand that AI can make false claims with complete certainty (never take medical or legal advice from AI without verification).

Privacy protection:

Never give sensitive data (social security number, passwords, bank details) to an AI.

Knowing that everything we write to the AI is potentially used to train it.

Spotting "Deepfakes":

How to recognize a doctored image or video (details on the hands, strange reflections, slightly metallic voice).

Verify the information: the golden rule of cross-referencing sources.

5: Ethics and Impacts (To go further)
Copyright: Who owns an image created by AI?

The environmental impact: The water and energy consumption of AI servers.

The future: Will AI replace us or assist us?
verified badge
Ammar
Artificial Intelligence and programming become much easier when you understand the reasoning behind the algorithms—not simply memorize Python syntax or use AI tools as a black box.

I am a PhD-qualified engineer, university professor, researcher, programmer, and multidisciplinary tutor with more than 30 years of experience across teaching, technical training, engineering, information systems, quantitative analysis, research, data mining, programming, and intelligent knowledge-based systems.

This class provides a structured and personalized pathway for beginners, school and university students, researchers, engineers, professionals, career changers, and adult learners. Depending on your goals, we can focus on Python programming, computational problem solving, automation, machine learning, artificial intelligence, or a coherent progression connecting them.

PYTHON PROGRAMMING & COMPUTATIONAL THINKING
• Python installation and development environments
• Variables, data types, operators, and expressions
• Input, output, and program flow
• Conditional statements and decision making
• For loops and while loops
• Functions, parameters, return values, and scope
• Strings and text processing
• Lists, tuples, sets, and dictionaries
• File handling and data input/output
• Error handling and exceptions
• Modules, packages, and reusable code
• Object-oriented programming
• Algorithms and computational problem solving
• Debugging and systematic error correction
• Code organization, readability, and good programming practices

SCIENTIFIC COMPUTING, DATA & AUTOMATION
• NumPy for numerical computing
• pandas for structured data manipulation
• matplotlib for visualization
• Scientific and engineering calculations
• Automation of repetitive tasks
• Data processing workflows
• Working with files and external data
• Introduction to APIs when relevant
• Python and SQL workflows
• Research and quantitative applications
• Project development from idea to working solution

MACHINE LEARNING
• Foundations of machine learning
• Supervised and unsupervised learning
• Regression and classification
• Clustering and pattern discovery
• Decision trees and rule-based approaches
• Feature selection and data preparation
• Training, validation, and testing
• Model evaluation and performance metrics
• Overfitting and underfitting
• Bias, variance, and generalization
• Model comparison and interpretation
• Predictive modelling and data mining
• Neural-network foundations

ARTIFICIAL INTELLIGENCE & INTELLIGENT SYSTEMS
• Foundations and major branches of Artificial Intelligence
• How intelligent systems represent, classify, predict, and support decisions
• Knowledge representation concepts
• Ontologies and structured knowledge
• Rule-based reasoning and expert-system foundations
• Intelligent decision-support systems
• Generative AI and large language model concepts
• Prompt design and effective AI-assisted workflows
• AI limitations and hallucinations
• Bias, privacy, ethics, and responsible AI
• Applications in engineering, research, business, education, and professional decision making

Depending on your goals, practical work may involve Python, NumPy, pandas, matplotlib, relevant machine-learning libraries, Weka, SPSS Modeler, structured datasets, or modern generative-AI tools.

My teaching approach follows a clear progression:
understand the problem → design the logic → represent and prepare the data → write or select the method → test it → evaluate the output → debug or improve it → interpret the result → apply it responsibly

I do not simply provide finished code, demonstrate isolated commands, or recommend an AI model because it is popular. I help you understand why a method works, what assumptions it makes, when it should be used, how to evaluate its results, where it may fail, and how to improve the solution.

We can work with your course syllabus, programming exercises, existing code, error messages, dataset, research problem, AI project, automation task, engineering application, model output, or professional use case.

Whether you are writing your first Python program, preparing for a university course, debugging a project, automating a professional task, learning machine learning, or exploring advanced AI applications, I will adapt the sessions to your level, objectives, and pace.

My goal is to help you become an independent computational problem solver who can understand, build, evaluate, and apply intelligent solutions—not merely copy code or use AI tools without understanding them.
Good-fit Instructor Guarantee
favorite button
message icon
Contact Reza