Lectures lectures
Regular classes  Tuesday (12:00  13:30) and Thursday (14:00  16:30)Date  Topic and Reference  Resources 

17012017  Review of Linear Algebra  Gilbert Strang Lectures 
24012017  Simple Linear Regression
Read : Section 3.1 of ISLR 
ISLR Lectures (Chapter 3)
Advertising.csv 
30012017  Simple Linear Regression
Read : Section 3.1 of ISLR 
ISLR Lectures (Chapter 3)
SimpleLinReg.R 
02022017  Simple & Multiple Linear Regression
Read : Sections 3.1 & 3.2 of ISLR 
ISLR Lectures (Chapter 3)
MultipleLinReg.R 
04022017  Multiple Linear Regression
Read : Sections 3.2 to 3.4 of ISLR 
ISLR Lectures (Chapter 3)
MultipleLinReg.R 
07022017  Gradient Descent Algorithm
Task : Assignment 1 
Ng Lectures (W1 08 to 10)
Ng Lectures (W2 02 to 04) 
14022017  Linear Regression Lab
Read : Section 3.6 of ISLR 
ISLR Lectures (Chapter 3)
LinRegLab.R 
16022017  Linear Regression Lab
Read : Section 3.6 of ISLR 
ISLR Lectures (Chapter 3)
LinRegLab.R 
21022017  Cross Validation
Read : Sections 5.1 & 5.3 of ISLR 
ISLR Lectures (Chapter 5)
CrossVal.R 
02032017  Singular Value Decomposition
Read : Gilbert Strang's Paper 
Linear Algebra Slides
SVdecomp.R 
07032017  SVD (contd.) and Classification
Read : Sections 4.1 & 4.2 of ISLR 
ISLR Lectures (Chapter 4)
SVdecomp.R 
09032017  Logistic Regression
Read : Section 4.3 of ISLR 
ISLR Lectures (Chapter 4)
SimpleLogReg.R 
14032017  Logistic Regression Lab
Read : Sections 4.3 & 4.6 of ISLR 
ISLR Lectures (Chapter 4)
SimpleLogReg.R 
16032017  Recap for MidSem (full syllabus)  
18032017 MidSemester Examination 
Question Paper
wineTrain.csv pimaTrain.csv 

21032017  Decision Tree for Classification
Read : Section 8.1 of ISLR 
ISLR Lectures (Chapter 8)
DecisionTree.R 
23032017  Decision Tree Lab
Read : Section 8.1 of ISLR 
DecTreeLab.R
CarEvaluation.csv Advertising.csv 
25032017  Pruning a Decision Tree
Read : Section 8.1 of ISLR 
ISLR Lectures (Chapter 8)
PruneTree.R 
28032017  Bagging and Random Forest
Read : Section 8.2 of ISLR 
ISLR Lectures (Chapter 8)
RandomForest.R 
04042017  Boosting and Shrinkage
Read : Section 8.2 of ISLR 
ISLR Lectures (Chapter 8)
BoostShrink.R 
06042017  Boosting and Shrinkage
Read : Sections 6.1 & 6.2 of ISLR 
ISLR Lectures (Chapter 6)
BoostShrink.R 
10042017  Maximal Margin Classifier  MIT OCW Lecture (SVM) 
11042017  Maximal Margin and Support Vectors
Read : Sections 9.1 & 9.2 of ISLR 
ISLR Lectures (Chapter 9)
SupportVector.R 
18042017  Support Vector Machines
Read : Sections 9.3 & 9.4 of ISLR 
ISLR Lectures (Chapter 9)
SVMachineLab.R 
20042017  Clustering Methods
Read : Section 10.3 of ISLR 
ISLR Lectures (Chapter 10)
Clustering.R 
25042017  Dimensionality and Recommendations
Reference : Chapters 9 & 11 of MMDS 
Andrew Ng Lectures
DimReduction.R 
27042017  Data Scraping using R 
DataScraping.R
imdbLinks.csv 
04052017  Recap for EndSem (full syllabus)  
06052017 EndSemester Examination  11 AM to 5 PM 
Evaluation evaluation
Tests
The tests constitute 80% of the total marks. The tentative format and breakup is as follows.Test  Weightage  Format  Resources 

MidSem  30%  Openresources written test 
Question Paper
wineTrain.csv pimaTrain.csv 
EndSem  50%  Openresources laboratory test  To be posted 
Assignments
Assignments constitute 20% of the total marks.This includes 5% for group scribing of lecture notes, and 15% for group submission of assignments.
Assignment  Posted  Deadline  Topic (Marks) 

Assignment 1  16022017  28022017  Linear Regression (50) 
Assignment 2  31032017  11042017  Binary Classification (50) 
Assignment 3  20042017  04052017  SVM and Clustering (50) 
Course Information
Machine Learning, RKM Vivekananda UniversityMSc Data Science, MSc Computer Science
Instructor  Sourav Sen Gupta, ISI Kolkata
sg.sourav@gmail.com (preferred)
+91 94323 44852 (if it is urgent)
Room 404, Deshmukh Building, ISI Kolkata
Text and References
T1  ISLR
An Introduction to Statistical LearningJames, Witten, Hastie, and Tibshirani
Book (in PDF) available online at this link
Lecture videos available online at this link
T2  MMDS
Mining of Massive DatasetsLeskovec, Rajaraman, and Ullman
Book (in PDF) available online at this link
R1  ESL
The Elements of Statistical LearningHastie, Tibshirani, and Friedman
Book (in PDF) available online at this link
Resources
Review Linear Algebra
Linear Algebra Lectures by Gilbert StrangLearn to Code in Python
Learn Python the Hard Way by Zed A. ShawGoogle's Python Class (with video lectures)
Machine Learning in Python
scikitlearn  Machine Learning in PythonML Video Tutorials from Data School
Data Science Challenges
Data Science Competitions by KaggleData Science Competitions by DrivenData
Data Repositories
Data Sets corresponding to the ISLR bookMachine Learning Repository by UC Irvine
Datasets for Data Science by KDnuggets