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: NC-231
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 NC-231.
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 |
Counting sort and Radix sort slides |
|
13 |
4/3 |
Same as above. |
|
14 |
7/3 |
Heapsort and Priority queue. slides |
|
15 |
11/3 |
Same as above. |
|
16 |
14/3 |
Balanced binary search trees. slides |
|
17 |
18/3 |
Balanced binary search trees. slides |
|
18 |
21/3 |
Balanced binary search trees. slides |
|
19 |
25/3 |
Longest common subsequence, Dyanmic programming slides |
|
20 |
28/3 |
Longest common subsequence, Dyanmic programming slides |
|
21 |
1 / 4 |
Graphs, Minimal Spanning Trees slides |
|
22 |
4/4 |
Graphs, Minimal Spanning Trees slides |
|
23 |
8/4 |
Dijkstra's Shortest Path Algorithm slides |
|
24 |
11/4 |
Bellman-Ford and Floyd-Warshall Algorithm slides |
|
24 |
15/4 |
Bellman-Ford and Floyd-Warshall Algorithm slides |
|
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 |
Non-parametric classifier, k-NN, Decision trees slides |
|
5 |
5/2 |
Bagging, Random forests. slides |
|
6 |
12/2 |
Boosting, Support vector machines. slides |
|
7 |
5/3 |
Feed-forward neural networks slides |
|
8 |
12/3 |
Hands-on session with tensorflow and Feed forward neural network. |
|
9 |
19/3 |
Recurrent Neural Network, attention models. slides |
|
10 |
26/3 |
Recurrent Neural Network, attention models. slides. |
|
11 |
2/4 |
Convolutional Neural Networks. slides |
|
12 |
9/4 |
Stochastic gradient descent, Active Learning. slides |
|
13 |
16/4 |
Multi-task learning, Reinforcement learning. slides |
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 NP-completeness- basic notions
Introduction to randomized algorithms and approximation algorithms.
Notion of sub-linear 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 semi-supervised 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