Pattern Recognition (ICS-702) crash course, Online B.Tech, Semester 7:
Clustering (criterion functions, validation, k-means/hierarchical/DBSCAN) and fuzzy classification each appear in all three parts; add a general essay on…
- 6 video lessons
- 1h 59m
- Study notes
- 25 quiz questions
- Assignment
6 lessons in Pattern Recognition
Each lesson pairs one topic with a hand-picked video, trimmed to the part that teaches it.
About this subject
Clustering (criterion functions, validation, k-means/hierarchical/DBSCAN) and fuzzy classification each appear in all three parts; add a general essay on classifiers vs models, plus Bayes probability and PCA/LDA for short answers.
- 1
Clustering: algorithms, criterion functions, validation
Must study for the exam (×4 · A9, A10, B5, C1 2 + 2 + 5 + 15 marks · MCQ, short note, long explanation).
Complete Unsupervised Machine Learning Tutorials In Hindi- K Means,DBSCAN, Hierarchical Clustering· Krish Naik Hindi· 63 min
Study notes 5 quiz questionsWatch lesson - 2
Classifiers: approaches and role in automation
Must study for the exam (×3 · A1, A7, C3 2 + 2 + 15 marks · MCQ, long explanation).
Pattern Recognition· Anindita Das Bhattacharjee· 8 min
Study notesWatch lesson - 3
Fuzzy classification and fuzzy rule-based classifiers
Must study for the exam (×3 · A8, B4, C2 2 + 5 + 15 marks · MCQ, short note, long explanation).
Lec-33: Fuzzy Logic in Artificial Intelligence with Example | Artificial Intelligence· Gate Smashers· 13 min
Study notes 5 quiz questionsWatch lesson - 4
Dimensionality reduction: PCA and Fisher LDA
High value for the exam (×3 · A5, A6, B3 2 + 2 + 5 marks · MCQ, short note).
Lec-46: Principal Component Analysis (PCA) Explained | Machine Learning· Gate Smashers· 14 min
Study notes 5 quiz questionsWatch lesson - 5
Probability, Bayes and the normal density
High value for the exam (×3 · A3, B1, B2 2 + 5 + 5 marks · MCQ, definition, numerical).
Bayes Theorem Explained with Solved Example in Hindi ll Machine Learning Course· 5 Minutes Engineering· 11 min
Study notes 5 quiz questionsWatch lesson - 6
Data scales and feature selection
If time permits for the exam (×2 · A2, A4 2 + 2 marks · MCQ).
Measurement scales Nominal Ordinal Interval Ratio Research Methodology in hindi· Study Lovers Kapil Gangwani· 10 min
Study notes 5 quiz questionsWatch lesson
Assignment
Pattern Recognition Exam Problem Set: Bayes, PCA/LDA, Fuzzy Rules, Clustering and Feature Selection
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Hand-picked video lessons
6 short lessons, each with the best YouTube explanation we could find, trimmed to the part that matters.
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Study notes
6 lessons come with a summary, key points, key terms and takeaways for quick revision.
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Quizzes
25 questions across the course, with the right answer and why, the moment you pick.
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Assignments
1 subject assignments with clear tasks, so you practise what each subject actually asks of you.
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Notebook
Write notes beside the video, stamped with the exact time, and jump back to that moment later.
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Progress that follows you
Completed lessons, quiz scores and where you stopped, saved to your account on every device.
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Live chat
A members-only live chat beside every lesson: doubts, tips, stickers and GIFs with your batch. One ₹19 pass opens every live chat.
The Online B.Tech live chat. Study together, live.
A live chat right beside the video, shared by everyone studying Online B.Tech. Ask a Pattern Recognition doubt mid-lesson, share a shortcut, celebrate a finished semester.
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Anyone else finding Pattern Recognition tricky? 😅
Lesson 2 cleared it up for me. Watch it before the quiz
Doing it now, thanks!
Starting Channel Hydraulics tonight if anyone wants to go together
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Pattern Recognition: common questions
Clustering (criterion functions, validation, k-means/hierarchical/DBSCAN) and fuzzy classification each appear in all three parts; add a general essay on classifiers vs models, plus Bayes probability and PCA/LDA for short answers. It's the Pattern Recognition subject of Online B.Tech, Semester 7, in 6 lessons: Clustering: algorithms, criterion functions, validation; Classifiers: approaches and role in automation; Fuzzy classification and fuzzy rule-based classifiers; Dimensionality reduction: PCA and Fisher LDA; Probability, Bayes and the normal density; Data scales and feature selection.
Yes. Every video lesson is free to watch without an account. A free login unlocks study notes, quizzes, the assignment, a notebook and progress that syncs across devices.
About 1h 59m of video across 6 lessons, plus time for notes and quizzes. Go at your own pace; your place is saved.
Yes: “Pattern Recognition Exam Problem Set: Bayes, PCA/LDA, Fuzzy Rules, Clustering and Feature Selection”. It comes after the last lesson, with a brief, clear tasks and how the work is judged.
A members-only live chat beside the video, shared by everyone studying Online B.Tech, where learners ask doubts, share tips and react with stickers and GIFs. One ₹19 pass, paid once from your InsideEdge wallet, opens the live chat for every degree.
No. It's a crash course to help you understand and revise the subject quickly. Use it alongside your official study material and assignments.