Inductive Bias & Machine Learning (Based on output)
7
Types of Machine Learning (Based on Input)
8
Types of Machine Learning (Based on Input)
Week 3
9
Supervised Learning - How it works?
10
How are ML algorithms different?
11
Linear Regression
12
Linear Regression and Gradient Descent
Week 4
13
Mathematics for ML (Exercises)
14
Ananconda and Numpy (Tutorial)
15
Scikit Learn Linear Regression (Tutorial)
16
Linear Regression with Gradient Descent
Week 5 (Sept 6 - Sept 10)
NA
Project Proposal Presentations
Week 6
17
Logistic Regression
18
Unsupervised algorithm - kMeans Clustering
19
Unsupervised algorithm - DBSCAN Clustering
20
Clustering (Tutorial)
Week 7
21
kNN
22
Decision Trees - Classification
23
Decision Trees - Regression
24
Decision Tree (Tutorial)
Week 8, 9
Midterm
Week 10 (Oct 11 - Oct 15)
NA
Project Midway Presentations
Week 11
25
Support Vector Machine
26
Support Vector Machine - Kernels
27
Feature Engineering
28
Bias-Variance Tradeoff, Regularization
Week 12
29
Model Assessment
30
Perceptron (Convergence)
31
Perceptron Implementation
32
Multi-Layer Perceptron
Week 13, 14 (Nov 1 - Nov 12)
NA
Project Final Presentations
Week 15
33
Ensemble Method - Bagging
34
Ensemble Method - Boosting
35
Unsupervised - Dimensionality Reduction PCA
Week 16
NA
End Term
Assignments
[05] Assign 1: Lecture Note Videos
Group of 2
Video length of 30-45 mins
Both the partners have equal contribution
[05] Homeworks
Project
A group of 2-3. Class project must be something new that you did in this semester. Two types of projects are
allowed
Category - 1: You pick an interesting dataset, apply best suited one or more well known machine
learning algorithms as baselines and extend these baselines in your creative and intersting way.
Category - 2: A theoretical project should look at an open question, definite it concretely, look at
the literature, design and develop createive and interesting attempts.
All deliverables (reports) will be in form of static webpages with the same template as this website (single
scroll). It will be linked from this website and hosted on the NISER server. Please maintain a single github
repository and submit the same in Classroom.
[05] (Max. 5 slides) Project Proposal - 10 mins
Title, dataset, idea, relevant papers, teammate with work division, what to do by Midway, what
baselines to implement, what are the expected results
[10] (Max. 10 slides) Project Midway - 15 mins + 5 mins Q&A
Insightful analysis of 2-3 related papers.
Experiments you have done along with results.
[10] (Max. 15 slides) Final Project Presentation - 20 mins + 10 mins for Q&A
Review
New and interesting attempts to solve the problem
[15] Final Project Report
[05] Extra Credit - Project yields significant result [Paper has been communicated to a
conference/journal]