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Syllabus
Introduction, Logistic regression, Perceptron,
Generative learning algorithm: Support vector machines, Model selection and feature selection, Ensemble methods: Bagging, boosting, Random Forest;
Unsupervised learning: Clustering: K-means, EM; Mixture of Gaussians, Factor analysis, PCA (Principal components analysis).;
Active learning: Theoretical analysis, Committee-based active learning, Active learning from the crowd;
Collaborative filtering: Latent factor-based models and neighborhood models;
Introduction to Graphical Models (HMM, MEMM, CRF), Deep Learning: CNN, RNN, LSTM, GRU.
Reference Books:
- T. Mitchell. Machine Learning. McGraw-Hill, 1997
Machine Learning (ufpe.br)
Machine Learning textbook (cmu.edu) - Christopher Bishop. Pattern recognition and machine learning. Springer Verlag, 2006.
Pattern Recognition and Machine Learning (microsoft.com) - Hastie, Tibshirani, Friedman. The elements of Statistical Learning Springer Verlag.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition | SpringerLink (You can purchase this book from here) - Probability, Random Variables and Stochastic processes by Papoulis and Pillai, 4thEdition, Tata McGraw Hill Edition.
- A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Prentice Hall, 198815.
Classes
(You may need to login into IITP LMS for some classes. Better to login in one tab and then access these resources)
| Lecture # | Link to Class | Topic Covered | Remarks |
|---|---|---|---|
| 1 | Class : 29 July 2023 | Introduction to ML | |
| 2 | Class :17 May 2023 | ||
| 3 | Class : 31 July 2023 | Introduction to Supervised, Unsupervised, Reinforcement Learning | |
| 4 | Class : 3 Aug 2023 | ||
| Weekend Class : 5 Aug 2023 | |||
| 5 | Class : 7 Aug 2023 | Cluster Analysis: Basic Concept & Analysis Types of Clusters k-mean, SSE | |
| 6 | Class : 9 Aug 2023 | K-means in detail | |
| 7 | Class : 10 Aug 2023 | K-means, K-medoids | |
| Weekend Class : 12 Aug 2023 | |||
| 8 | Class : 14 Aug 2023 | Hierarchical Clustering, Agglomerative Algorithm | |
| 9 | Class : 16 Aug 2023 | Hierarchical Clustering, DBSCAN | |
| 10 | Class : 17 Ag 2023 | DBSCAN | |
| Weekend Class : 20 Aug 2023 | |||
| Weekend Class : 20 Aug 2023 | |||
| 11 | Class : 21 Aug 2023 | DBSCAN | |
| 12 | Class : 23 Aug 2023 | ||
| 13 | Class : 24 Aug 2023 | ||
| Weekend Class: 26 Aug 2023 | |||
| 14 | Class : 28 Aug 2023 | ||
| 15 | Class : 30 Aug 2023 | ||
| 16 | Class : 31 Aug 2023 | ||
| Weekend Class: 2 Sep 2023 | |||
Topic wise Learning Material:
| Topic | Link | Remarks |
|---|---|---|
| K-Mean | Machine Intelligence – Lecture 7 (Clustering, k-means, SOM) – YouTube — very good explanation K Means Clustering Algorithm | Edureka – YouTube K Means Clustering Algorithm | Simplilearn – YouTube | |
| K-Mediod & DBSCAN | IITM – Lec-27 K-Medoids and DBSCAN – YouTube | |
| Supervised, Unsupervised, Reinforcement Learning | Supervised vs Unsupervised vs Reinforcement Learning | Simplilearn – YouTube | Introduction with examples |
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