# Unit 1 · Gaussian mixture models and EM

Subject: Intelligent Systems II

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/is2/#tee/read/u1-gmm

Canonical: https://deep-learning-study-room.vercel.app/read/is2/tee/read-chapter/u1-gmm/

Unit 1 · Mixture models, GMM, expectation maximization, clustering using GMM · 10-mark PYQ in both papers

1. [Before GMM: centre, spread, density and likelihood](https://deep-learning-study-room.vercel.app/read/is2/tee/read/u1-gmm--centre-spread/)
2. [GMM prerequisites: mean, variance, density and mixture](https://deep-learning-study-room.vercel.app/read/is2/tee/read/u1-gmm--mixture-models/)
3. [EM for GMM: every responsibility and parameter update](https://deep-learning-study-room.vercel.app/read/is2/tee/read/u1-gmm--em/)
4. [10-mark answer: GMM vs k-means, log-likelihood derivation, parameters for K = 5](https://deep-learning-study-room.vercel.app/read/is2/tee/read/u1-gmm--ten-mark-answer/)
5. [Clustering with GMM: uses, advantages and limits](https://deep-learning-study-room.vercel.app/read/is2/tee/read/u1-gmm--clustering-applications/)