Machine Learning (CS 564)

Link to Main Page

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:

1.  T. Mitchell. Machine Learning. McGraw-Hill, 1997
   Machine Learning (ufpe.br)
   Machine Learning textbook (cmu.edu)
2.  Christopher Bishop. Pattern recognition and machine learning. Springer Verlag, 2006.
   Pattern Recognition and Machine Learning (microsoft.com)
3.  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)
4.  Probability, Random Variables and Stochastic processes by Papoulis and Pillai, 4thEdition, Tata McGraw Hill Edition.
5.  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

Link to Main Page

---

The links mentioned on this page does not belong to us. These are property of the owner of those links. If you have any objection, then please send us a message.