CS6510: Applied
Machine Learning
Fall 2016
Instructor: Vineeth N
Balasubramanian
Class Schedule: Sat 9:00 am –
12:30 pm
Class Location: LH1
TAs:
Arghya Pal, Supriya Pandhre, Mausam Jain, Akilesh B, Hrishikesh Vaidya, Sahil
Manocha
OBJECTIVE: This course
will introduce core statistical machine learning methods with a relatively
lowmath approach, emphasizing applied problemsolving and engineering. At the
end of the course, the student will have a sound understanding of various
machine learning methods for prediction tasks, their intuition, and how they
can be applied to realworld problems.
TOPICS (Tentative): Classification (Naive Bayes, kNN, SVM, Neural
Networks, Decision Trees, Logistic Regression, Ensemble Methods), Regression
(Linear, Nonlinear, kNN, SVR), Clustering (kmeans, DBSCAN, hierarchical),
Dimensionality Reduction (PCA, MDS, Isomap), Gaussian Mixture Models, EM,
Feature Selection, Model Selection and
Performance Evaluation (CrossValidation, Bootstrap, ROC), Time series analysis
methods
The course will
also have a significant handson component involving inclass demonstrations,
practice exercises and Kagglestyle class contests (and potentially an ML
hackathon conducted by Microsoft).
ELIGIBILITY: This
course is intended for EMDS 2^{nd} year students, and BTech 3^{rd}
year students (CSE/ES)
PREREQUISITES:
Probability, Linear Algebra, Calculus
LECTURES: Lec1 – Intro (PDF)
REFERENCES:
1. Witten,
I. H., Frank, E., and Hall, M. (2011). Data Mining: Practical Machine
Learning Tools and Techniques, third edition, Elsevier: San Francisco, ISBN
978012374856 0 (The book behind Weka, the popular
ML toolkit)
2.
Pattern
Recognition and Machine Learning by C. Bishop
OTHER USEFUL
RESOURCES:

Elements of Statistical
Learning by Hastie, Tibshirani and Friedman
 The lecture notes from the old Learning from
Data course are useful, although they contain more mathematical detail than we
will do in this course.
 A Few Useful Things to Know about Machine Learning by
P. Domingos
 Coursera course on Machine Learning by Andrew Ng