Our Artificial Intelligence course covers intelligent agents, uninformed and informed search, adversarial search, constraint satisfaction, logic and knowledge representation, reasoning under uncertainty and Bayesian networks, planning, machine learning basics, neural networks and the ethics of AI. Lessons are live and one-to-one with a verified subject expert, who explains each concept with worked examples, solves problems from your own module and helps you prepare for exams and coursework. Suitable for computer science and engineering students, following university syllabi in the UK, US, Australia and India (including AKTU, VTU and Anna University style courses).
Reviewed by K. Sardar, Bachelor's degree in Data Science; data science and AI specialistLast updated
$14 – $37
per hour
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What you will learn
Prerequisites
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Course structure
In short: this course gives one-to-one online tutoring in artificial intelligence. A verified tutor explains search, heuristics, game playing, knowledge representation, probabilistic reasoning and machine learning foundations, with worked problems and code.
Artificial intelligence (AI) is the field of computer science concerned with building systems that perceive, reason, learn and act to achieve goals. A university AI course covers intelligent agents, problem solving by search, game playing, constraint satisfaction, logic and knowledge representation, reasoning under uncertainty, planning and an introduction to machine learning and natural language.
This online course teaches Artificial Intelligence one to one with a verified tutor who has studied or taught it at university level, at your pace and aligned with your own module.
Related subject: Artificial intelligence help.
| Topic | Key content |
|---|---|
| Fundamentals | Agents, environments, problem formulation |
| Search | Uninformed search (BFS, DFS, uniform cost), heuristic search (greedy, A*), adversarial search (minimax, alpha-beta) |
| Constraint satisfaction | Backtracking, forward checking, arc consistency |
| Knowledge representation | Propositional and predicate logic, inference rules, unification, resolution, semantic networks |
| Prolog | Facts, rules, queries and answer extraction |
| Uncertainty | Probability, Bayes' rule, Bayesian networks, dynamic Bayesian networks |
| Planning | State-space planning, planning graphs |
| Learning and language | Inductive learning, decision trees, stages of language analysis, parsing |
A* expands nodes in order of f(n) = g(n) + h(n), where g is the path cost so far and h is a heuristic estimate of the remaining cost. If the heuristic is admissible (never overestimates) for tree search, or consistent for graph search, A* is guaranteed to find an optimal solution, and a better-informed heuristic expands fewer nodes.
Here is a small example: a MAX root has two MIN children. The first MIN node has leaves 3 and 5; the second has leaves 2 and 9.
| Step | Working |
|---|---|
| First MIN node | min(3, 5) = 3, so alpha = 3 at the root |
| Second MIN node, first leaf | 2, so this node's value is at most 2 |
| Prune | 2 ≤ alpha (3), so leaf 9 is never examined |
| Root value | max(3, ≤2) = 3 |
Students most often lose marks in Artificial Intelligence through the habits below. Each is easy to fix with practice and feedback on your own answers.
| Mark-loser | What to do instead |
|---|---|
| Heuristics not checked for admissibility | Show the heuristic never overestimates |
| Search traces with wrong order | Track the frontier step by step |
| Pruning errors | Update alpha and beta at every node |
| Logic translation errors | Check quantifiers and scope carefully |
| Probability rules misapplied | Write Bayes' rule explicitly |
AI modules are usually assessed by written exams on search traces, logic and probabilistic reasoning, plus programming assignments such as search agents or game-playing programs in Python or Prolog.
Tutors explain concepts, work through practice problems with you and give feedback on your approach. Graded work you submit must be your own, as set out in our academic integrity policy. For deadline-driven guidance, see assignment help.
Lessons are live and one to one on video with screen sharing and a shared whiteboard. You can follow a full course or book sessions on the topics you find hardest before exams. See also Machine Learning, Computer Vision and our Artificial Intelligence subject page.
An artificial intelligence tutor online gives you live 1-to-1 lessons on search, logic, probability and planning, working through the questions your own module sets.
This artificial intelligence course suits undergraduate computer science and engineering students taking an AI module, and graduates who want a structured, exam-focused route through classical AI before moving on to machine learning.
Prerequisites: basic programming, data structures and algorithms, discrete mathematics and basic probability. If any of these are weak, your tutor starts with a short refresher.
Course length: 40 one-hour lessons across 8 modules, taken at the pace your term needs.
| Stage | Modules | What you can do afterwards |
|---|---|---|
| 1 | Fundamental concepts, search techniques | Trace BFS, DFS, uniform-cost and A* search by hand |
| 2 | Knowledge representation, Prolog | Write propositional and first-order logic, run simple Prolog queries |
| 3 | Bayesian reasoning, planning | Compute probabilities in a Bayesian network, build a simple plan |
| 4 | Learning, natural language and advanced topics | Explain core learning methods and sit past papers under timed conditions |
Where it leads: most students continue with our Machine Learning course or Python for Data Science.
Building systems that perceive, reason, learn and act to achieve goals.
Machine learning is one part of AI; AI also covers search, logic, planning and reasoning.
One that never overestimates the true cost to reach the goal.
An optimisation of minimax that skips branches that cannot affect the final decision.
A graph of variables and conditional probabilities used to reason under uncertainty.
Many AI modules include Prolog for logic programming; tutors can teach it.
Tutors explain algorithms and review your code; the work you submit must be your own.
Yes. Tutors practise search traces, logic and probability questions.
The price depends on the tutor and session length. You see the hourly rate before booking, with no subscription.
No. Tutors explain, guide and give feedback so you can complete your own work, in line with our academic integrity policy.
The ideas are approachable, but AI courses combine programming, probability, linear algebra and search algorithms, so gaps in any of these slow you down. Students who are comfortable with Python and basic maths usually progress steadily. A tutor helps you link the theory, such as search and learning algorithms, to working code and exam questions.
BFS explores level by level and finds the shallowest goal; DFS goes deep first and uses less memory.
A logic with predicates, variables and quantifiers for representing knowledge.
A problem of assigning values to variables that satisfy given constraints, such as map colouring.
Anyone with basic programming and school-level maths can start an artificial intelligence course. University AI modules usually expect data structures, discrete mathematics and some probability first. If you are missing one of these, your tutor covers the gap before the main topics, so you are not lost in search or probability lessons.
A typical university artificial intelligence course covers search algorithms and heuristics, game playing with minimax and alpha-beta pruning, constraint satisfaction, logic and knowledge representation, probability and Bayesian networks, planning, and an introduction to machine learning. Many courses add Python programming tasks. Your tutor follows your own module outline and past exam papers.
Yes. Lessons are online, so you can book times that suit your time zone.
Weekly lessons suit most students; add extra sessions before mocks and final exams.
Yes. You can book one session to see if the tutor suits you, with no subscription.
Yes. With steady weekly study, most students can learn the core ideas of artificial intelligence in a year: search, logic, probability, basic machine learning and Python. Becoming job-ready in a specialism such as deep learning usually takes longer and needs projects. Your tutor builds a realistic plan around your course and goals.
Yes. You can learn with a 1-to-1 artificial intelligence tutor online through live video lessons with a shared whiteboard. Your tutor traces search algorithms by hand with you, explains logic and Bayesian networks step by step, helps you understand Python coursework, and plans your revision around your exam dates.
Keep learning
Artificial Intelligence specialists
Every expert is degree-verified and quality-tested before joining.
I am Pooja Nair, a Deep Learning & Computer Vision specialist with expertise in Python, TensorFlow, PyTorch, Neural Networks, CNNs, Image Processing, Object Detection, Classification, and Transfer Learning. I support students, researchers, and professionals with assignments,…
I am an experienced Cyber Security, Artificial Intelligence, and Drone Warfare Engineer with more than 4 years of professional experience. My expertise covers cyber security, AI, computer networks, cryptography, information security, Linux programming, prompt engineering,…
I am a BITS Pilani graduate in Electronics and Electrical Engineering, with minors in Mathematics and Physics and a strong focus on Artificial Intelligence, Machine Learning, and Agentic AI. I have 7 years of experience providing tutoring, academic project guidance, and thesis…
I'm Amit Kumar, a Data Scientist with 7 years of experience in Quantitative Research, Financial Analytics, and Risk Management. A graduate of IIT Kharagpur with a dual degree (B.Tech + M.Tech), I specialize in applying advanced mathematical and statistical concepts to solve…
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