Information Retrieval Course
Our Information Retrieval course covers inverted indexes and index construction, Boolean retrieval, the vector space model and TF-IDF, probabilistic models and BM25, evaluation measures such as precision, recall and MAP, query expansion, web crawling, link analysis with PageRank and learning to rank. 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 students at undergraduate and master's level, 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 – $42
per hour
Free demo class · pay only if you continue
What you will learn
Learning outcomes
- Understand information retrieval fundamentals
- Apply text preprocessing techniques
- Build and use inverted indexes
- Understand Boolean retrieval models
- Apply TF-IDF weighting
- Understand vector space models
- Process and optimize search queries
- Apply document ranking techniques
- Evaluate retrieval systems using precision and recall
- Understand web search and information retrieval applications
Prerequisites
- Basic programming
- Data structures and algorithms
- Database fundamentals
- Basic probability and statistics
- Computer science fundamentals
Tags
Course structure
Syllabus(8 modules)
- Information retrieval problem
- Inverted index
- Boolean query processing
- Extended Boolean model
- Ranked retrieval
What is Information Retrieval?
In short: this course gives one-to-one online tutoring in information retrieval. A verified tutor explains indexing, Boolean and vector space models, tf-idf, probabilistic ranking, evaluation and web search, with worked problems.
Information retrieval (IR) is the study of finding relevant documents, such as web pages or articles, from large collections in response to a user's query. It underpins search engines and covers indexing, query processing, ranking models such as tf-idf and probabilistic retrieval, evaluation measures, text classification, clustering and web search.
This online course teaches Information Retrieval 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: Computer science help.
What does the Information Retrieval course cover?
| Topic | Key content |
|---|---|
| Boolean retrieval | Inverted index, Boolean query processing, ranked retrieval |
| Vocabulary and postings | Tokenisation, stemming, skip pointers, positional indexes |
| Dictionaries and indexing | Wildcard queries, spelling correction, index construction and compression |
| Scoring and evaluation | Term weighting, tf-idf, vector space model, precision, recall, MAP, nDCG |
| XML and probabilistic IR | XML retrieval, probability ranking principle, BM25, language models |
| Text classification | Naive Bayes, feature selection, vector space classification |
| Clustering and decomposition | K-means, hierarchical clustering, latent semantic indexing |
| Web search | Web graph, crawling, duplicates, link analysis and PageRank |
How is tf-idf calculated?
tf-idf weights a term in a document by how often it appears there and how rare it is across the collection.
- Term frequency tf = the count of the term in the document, often log-scaled as 1 + log tf.
- Inverse document frequency idf = log(N ÷ df), where N is the number of documents and df the number containing the term.
- tf-idf = tf × idf.
- Represent each document and query as a vector of tf-idf weights.
- Rank documents by cosine similarity with the query.
How do you evaluate a retrieval system?
- Collect queries with relevance judgements.
- Precision = relevant retrieved ÷ total retrieved.
- Recall = relevant retrieved ÷ total relevant.
- Combine with F-measure, or use ranked measures such as MAP and nDCG.
- Compare systems on the same test collection.
What does a worked evaluation question look like?
Here is a typical question: a system returns 5 documents, of which 3 are relevant, and there are 6 relevant documents in total.
| Measure | Working | Result |
|---|---|---|
| Precision@5 | 3 ÷ 5 | 0.6 |
| Recall | 3 ÷ 6 | 0.5 |
| F1 | 2 × 0.6 × 0.5 ÷ (0.6 + 0.5) | ≈ 0.55 |
Where do students lose marks in Information Retrieval?
Students most often lose marks in Information Retrieval through the habits below. Each is easy to fix with practice and feedback on your own work.
| Mark-loser | What to do instead |
|---|---|
| Precision and recall swapped | Precision divides by retrieved; recall by relevant |
| tf-idf miscalculated | State the log base and formula |
| Steps skipped | Show each step of the method |
| Definitions vague | Use precise technical definitions |
| No worked check | Verify the result with a small example |
| Diagrams missing | Draw the structure or flow before solving |
How is it assessed, and how can a tutor help?
IR modules are usually assessed by written exams with tf-idf, cosine similarity and evaluation calculations, plus programming assignments building an index and search engine.
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.
How the lessons work
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 Database Management Systems, Machine Learning and Python for Data Science.
Information retrieval courses: who this course is for, prerequisites and study plan
Information retrieval courses teach how search engines index documents and rank results, and this one does so through live 1-to-1 lessons built around your own module, with worked tf-idf, BM25 and evaluation questions.
This course suits third- and fourth-year computer science students and postgraduates taking an information retrieval or search engines module.
Prerequisites: programming, data structures and algorithms, database fundamentals and basic probability and statistics.
Course length: 36 one-hour lessons across 8 modules.
| Stage | Modules | What you can do afterwards |
|---|---|---|
| 1 | Boolean retrieval, term vocabulary and postings lists | Build an inverted index and answer Boolean queries |
| 2 | Dictionaries, tolerant retrieval and indexing | Handle wildcards, spelling errors and index construction |
| 3 | Scoring, evaluation, XML and probabilistic retrieval | Calculate tf-idf and cosine similarity, precision, recall and MAP |
| 4 | Classification, clustering, matrix decomposition, web search | Classify and cluster documents and explain PageRank |
Where it leads: many students add our Machine Learning course or Database Management Systems.
Information Retrieval Course: frequently asked questions
What is information retrieval?
Finding relevant documents in large collections in response to a query, as search engines do.
What is an inverted index?
A mapping from each term to the list of documents that contain it.
What is tf-idf?
A term weighting that combines term frequency in a document with rarity across the collection.
What is the difference between precision and recall?
Precision measures how many retrieved documents are relevant; recall measures how many relevant documents are retrieved.
What is PageRank?
A link-analysis algorithm that scores web pages by the links pointing to them.
What is BM25?
A widely used probabilistic ranking function.
Can you help with IR programming assignments?
Tutors explain methods and review your code; the work you submit must be your own.
Can you help me prepare for my IR exam?
Yes. Tutors practise ranking and evaluation calculations.
How much does an Information Retrieval tutor cost?
The price depends on the tutor and session length. You see the hourly rate before booking, with no subscription.
Can a tutor do my assignment for me?
No. Tutors explain, guide and give feedback so you can complete your own work, in line with our academic integrity policy.
What is precision?
The fraction of retrieved documents that are relevant.
What is recall?
The fraction of relevant documents that are retrieved.
What is cosine similarity?
The cosine of the angle between two vectors, used to rank documents against a query.
What is mean average precision?
The mean of average precision across queries.
Is information retrieval part of AI?
Information retrieval is usually treated as its own field of computer science, but it overlaps strongly with AI, machine learning and natural language processing. Classic retrieval uses inverted indexes, TF-IDF and the vector space model, while modern search adds learning to rank and neural embeddings. Your tutor covers both the classic and modern methods.
Do you offer lessons in my time zone?
Yes. Lessons are online, so you can book times that suit your time zone.
Which book is best for information retrieval?
The most widely used textbook is Introduction to Information Retrieval by Manning, Raghavan and Schütze. Many university modules follow its chapters on indexing, ranking and evaluation. Your tutor uses the book or notes your module sets and adds worked tf-idf, cosine similarity and precision and recall questions for exam practice.
Can I book a single lesson first?
Yes. You can book one session to see if the tutor suits you, with no subscription.
Can my tutor set practice between lessons?
Yes. Tutors can set questions and go through them in the next session.
Is information retrieval the same as a search engine?
Not exactly. Information retrieval is the field that studies how to find relevant information in large collections, and a search engine is one application of it. The same methods also power email search, library catalogues, legal discovery and recommendation systems, and retrieval steps in AI assistants that answer from documents.
What do information retrieval courses cover?
Information retrieval courses usually cover inverted indexes, Boolean and ranked retrieval, tf-idf and the vector space model, probabilistic models such as BM25, evaluation with precision, recall and mean average precision, text classification and clustering, and web search with PageRank. Your tutor focuses on the topics your own module examines.
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