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 |
Question & Data
Non-Disclosable |
|
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 |
Question & Data
Non-Disclosable |
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 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
scikit-learn -- 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