CS6510: Applied Machine Learning
Instructor: Vineeth N Balasubramanian
Class Schedule: Sat 9:00 am – 12:30 pm
Class Location: LH-1
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 low-math approach, emphasizing applied problem-solving 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 real-world problems.
TOPICS (Tentative): Classification (Naive Bayes, k-NN, SVM, Neural Networks, Decision Trees, Logistic Regression, Ensemble Methods), Regression (Linear, Non-linear, k-NN, SVR), Clustering (k-means, DBSCAN, hierarchical), Dimensionality Reduction (PCA, MDS, Isomap), Gaussian Mixture Models, EM, Feature Selection, Model Selection and Performance Evaluation (Cross-Validation, Bootstrap, ROC), Time series analysis methods
The course will also have a significant hands-on component involving in-class demonstrations, practice exercises and Kaggle-style class contests (and potentially an ML hackathon conducted by Microsoft).
ELIGIBILITY: This course is intended for EMDS 2nd year students, and BTech 3rd year students (CSE/ES)
PRE-REQUISITES: Probability, Linear Algebra, Calculus
LECTURES: Lec1 – Intro (PDF)
1. Witten, I. H., Frank, E., and Hall, M. (2011). Data Mining: Practical Machine Learning Tools and Techniques, third edition, Elsevier: San Francisco, ISBN 978-0-12-374856- 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