Lectures lectures

Regular classes -- Tuesday (12:00 - 13:30) and Thursday (14:00 - 16:30)

  Date   Topic and Reference   Resources
17-01-2017 Review of Linear Algebra   Gilbert Strang Lectures
24-01-2017 Simple Linear Regression
Read : Section 3.1 of ISLR
  ISLR Lectures (Chapter 3)
  Advertising.csv
30-01-2017 Simple Linear Regression
Read : Section 3.1 of ISLR
  ISLR Lectures (Chapter 3)
  SimpleLinReg.R
02-02-2017 Simple & Multiple Linear Regression
Read : Sections 3.1 & 3.2 of ISLR
  ISLR Lectures (Chapter 3)
  MultipleLinReg.R
04-02-2017 Multiple Linear Regression
Read : Sections 3.2 to 3.4 of ISLR
  ISLR Lectures (Chapter 3)
  MultipleLinReg.R
07-02-2017 Gradient Descent Algorithm
Task : Assignment 1
  Ng Lectures (W1 08 to 10)
  Ng Lectures (W2 02 to 04)
14-02-2017 Linear Regression Lab
Read : Section 3.6 of ISLR
  ISLR Lectures (Chapter 3)
  LinRegLab.R
16-02-2017 Linear Regression Lab
Read : Section 3.6 of ISLR
  ISLR Lectures (Chapter 3)
  LinRegLab.R
21-02-2017 Cross Validation
Read : Sections 5.1 & 5.3 of ISLR
  ISLR Lectures (Chapter 5)
  CrossVal.R
02-03-2017 Singular Value Decomposition
Read : Gilbert Strang's Paper
  Linear Algebra Slides
  SVdecomp.R
07-03-2017 SVD (contd.) and Classification
Read : Sections 4.1 & 4.2 of ISLR
  ISLR Lectures (Chapter 4)
  SVdecomp.R
09-03-2017 Logistic Regression
Read : Section 4.3 of ISLR
  ISLR Lectures (Chapter 4)
  SimpleLogReg.R
14-03-2017 Logistic Regression Lab
Read : Sections 4.3 & 4.6 of ISLR
  ISLR Lectures (Chapter 4)
  SimpleLogReg.R
16-03-2017 Recap for Mid-Sem (full syllabus)  
18-03-2017             Mid-Semester Examination   Question Paper
  wineTrain.csv
  pimaTrain.csv
21-03-2017 Decision Tree for Classification
Read : Section 8.1 of ISLR
  ISLR Lectures (Chapter 8)
  DecisionTree.R
23-03-2017 Decision Tree Lab
Read : Section 8.1 of ISLR
  DecTreeLab.R
  CarEvaluation.csv
  Advertising.csv
25-03-2017 Pruning a Decision Tree
Read : Section 8.1 of ISLR
  ISLR Lectures (Chapter 8)
  PruneTree.R
28-03-2017 Bagging and Random Forest
Read : Section 8.2 of ISLR
  ISLR Lectures (Chapter 8)
  RandomForest.R
04-04-2017 Boosting and Shrinkage
Read : Section 8.2 of ISLR
  ISLR Lectures (Chapter 8)
  BoostShrink.R
06-04-2017 Boosting and Shrinkage
Read : Sections 6.1 & 6.2 of ISLR
  ISLR Lectures (Chapter 6)
  BoostShrink.R
10-04-2017 Maximal Margin Classifier   MIT OCW Lecture (SVM)
11-04-2017 Maximal Margin and Support Vectors
Read : Sections 9.1 & 9.2 of ISLR
  ISLR Lectures (Chapter 9)
  SupportVector.R
18-04-2017 Support Vector Machines
Read : Sections 9.3 & 9.4 of ISLR
  ISLR Lectures (Chapter 9)
  SVMachineLab.R
20-04-2017 Clustering Methods
Read : Section 10.3 of ISLR
  ISLR Lectures (Chapter 10)
  Clustering.R
25-04-2017 Dimensionality and Recommendations
Reference : Chapters 9 & 11 of MMDS
  Andrew Ng Lectures
  DimReduction.R
27-04-2017 Data Scraping using R   DataScraping.R
  imdbLinks.csv
04-05-2017 Recap for End-Sem (full syllabus)  
06-05-2017             End-Semester Examination 11 AM to 5 PM

Evaluation evaluation

Tests

The tests constitute 80% of the total marks. The tentative format and break-up is as follows.

Test Weightage Format Resources
Mid-Sem 30% Open-resources written test   Question Paper
  wineTrain.csv
  pimaTrain.csv
End-Sem 50% Open-resources 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 16-02-2017 28-02-2017 Linear Regression (50)
  Assignment 2 31-03-2017 11-04-2017 Binary Classification (50)
  Assignment 3 20-04-2017 04-05-2017 SVM and Clustering (50)

 

  Course Information

Machine Learning, RKM Vivekananda University
  MSc 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 Learning
  James, Witten, Hastie, and Tibshirani
  Book (in PDF) available online at this link
  Lecture videos available online at this link

T2   --   MMDS

Mining of Massive Datasets
  Leskovec, Rajaraman, and Ullman
  Book (in PDF) available online at this link

R1   --   ESL

The Elements of Statistical Learning
  Hastie, Tibshirani, and Friedman
  Book (in PDF) available online at this link

  Resources

Review Linear Algebra

  Linear Algebra Lectures by Gilbert Strang

Learn to Code in Python

  Learn Python the Hard Way by Zed A. Shaw
  Google's Python Class (with video lectures)

Machine Learning in Python

  scikit-learn -- Machine Learning in Python
  ML Video Tutorials from Data School

Data Science Challenges

  Data Science Competitions by Kaggle
  Data Science Competitions by DrivenData

Data Repositories

  Data Sets corresponding to the ISLR book
  Machine Learning Repository by UC Irvine
  Datasets for Data Science by KDnuggets