Foundations of Algorithm Design and Machine Learning (CS60020)
Instructor: Sourangshu Bhattacharya
TAs: Nikhil Kumar Singh, Paramita Koley and Soumi Das
Class Schedule: Monday (12  12:55 pm) , Tuesday (10:00  11:55 am) , Thurs(8:00  8:55 am)
Classroom: NC231
Website: http://cse.iitkgp.ac.in/~sourangshu/coursefiles/cs60050_19S.html
Past course website: http://cse.iitkgp.ac.in/~sourangshu/coursefiles/cs60050_18S.html
Moodle: https://10.5.18.110/moodle/ (accessible only from inside institute network)
First Meeting: Thursday, 7th January 2019, at 12:00 am in NC231.
Lecture schedule and Notes:
Introduction: Introductory class on 7th Jan. slides
Lecture 
Date 
Topic 
1 
10/1 
Insertion sort slides 
2 
14/1 
Guest lecture by Prof. Saurabh Bagchi 
3 
17/1 
Order Notation slides 
4 
21/1 
Divide and Conquer  Mergesort slides 
5 
24/1 
Divide and Conquer  Powering, Binary Search, Master Theorem slides 
6 
28/1 
Divide and Conquer  Fibonacci numbers, Strassen's algorithm, Subarray sum slides 
7 
31/1 
Divide and Conquer  Strassen', Proof of Master Theorem. slides 
8 
4/2 
Same as above 
9 
7/2 
Quicksort and Analysis slides 
10 
11/2 
Same as above. 
11 
14/2 

12 
28/2 

13 
4/3 

14 
7/3 

15 
11/3 

16 
14/3 

17 
18/3 

18 
21/3 

19 
25/3 

20 
28/3 

21 
1 / 4 

22 
4/4 

23 
8/4 

24 
11/4 

Lecture 
Date 
Topic 
1 
8/1 
Regression, overfitting and regularisation. slides 
2 
15/1 
Guest lecture by Prof. Saurabh Bagchi 
3 
22/1 
Fisher's linear discriminant, Logistic Regression, Naive Bayes. slides 
4 
29/1 
Nonparametric classifier, kNN, Decision trees slides 
5 
5/2 
Bagging, Random forests. slides 
6 
12/2 
Boosting, Support vector machines. slides 
7 
5/3 
RNNs, LSTM – applications. 
8 
12/3 
CNNs – applications. 
9 
19/3 
Attention models, Auto encoders. 
10 
26/3 
Probabilistic models – Naïve bayes, CRFs, HMMs, etc. 
11 
2/4 
Clustering – latent variable models. 
12 
9/4 
Online learning, Stochastic gradient descent. 
13 
16/4 
Multitask and transfer learning. 



Syllabus:
Algorithms for data Science:
Introduction to the design and analysis of efficient algorithms, with an emphasis on data science
Notion of time and space complexity and order notation.
Lists. Hashing.
Graph and Basic graph algorithms: Definitions and representation, Reachability, Shortest path.
A brief exposure to Divide and Conquer and Dynamic programming
Introducing the concept of NPcompleteness basic notions
Introduction to randomized algorithms and approximation algorithms.
Notion of sublinear algorithms, streaming algorithms, sampling
Foundations of Machine Learning:
Introduction. Concept
learning. Hypothesis space. Inductive Bias. Learnability. Underfitting and
overfitting.
Feature Selection, Dimension Reduction
SVM and introduction to kernel methods.
Unsupervised and semisupervised learning.
Expectation maximization. Mixture of Gaussians.
Active learning, Learning with Imbalanced Data. Anomaly detection.
Ensemble methods.
A brief introduction to graphical models
Introduction to Deep Learning
Assignments:
Textbooks:
 Introduction to Algorithms by Thomas H Cormen, Charles E Leiserson, Ronald L Rivest and Clifford Stein
 Introduction to Machine Learning by Ethem Alpaydin