Welcome to "Introduction to Machine Learning 419(M)".
In this undergraduate-level course, you will be introduced to the foundations of machine learning along with a slew of popular machine learning techniques. This will also give you insights on how to apply machine learning to solve a new problem.
This course is open to any non-CSE undergraduate student who wants to do a minor in CSE. There are no prerequisites.
Time: Wednesdays, Fridays, 9.30 am to 10.55 am
Venue: CC 103
Instructor: Preethi Jyothi. You can email me at pjyothi [at] cse [dot] iitb [dot] ac [dot] in
All assignments should be completed individually. No form of collaboration is allowed unless explicitly permitted by the instructor. Anyone deviating from these standards of academic integrity will be reported to the department's disciplinary committee.
Course syllabus includes basic classification/regression techniques such as Naive Bayes', decision trees, SVMs, boosting/bagging and linear/logistic regression, maximum likelihood estimates, regularization, basics of statistical learning theory, perceptron rule/multi-layer perceptrons, backpropagation, brief introduction to deep learning models, dimensionality reduction techniques like PCA and LDA, unsupervised learning: k-means clustering, gaussian mixture models, selected topics from natural/spoken language processing, computer vision, etc. [The syllabus is subject to minor changes depending on how the course proceeds.]
|Jan 5, 2018||Machine Learning: What and why?||Lecture1.pdf||Chapter 1 of [SS-2017]|
|Jan 10, 2018||Linear Regression (Part I)||Lecture2.pdf||Chapter 3.1,3.2.1 of [TH-2009]|
|Jan 12, 2018||Linear Regression (Part II)||TBA||TBA|
|Jan 16, 2018||Assignment 1 released:
Due Jan 24, 2018
|Jan 17, 2018||Linear Classification:
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