CS4780/CS5780: Machine Learning [FALL 2015]
Who:
Associate Prof. Kilian Weinberger
When/where:
Course Number: CS4780/CS5780
Times: Mo/We/Fr 1:25pm-2:15pm
First day, last day: 08/26/2015-12/04/2015
Room: Ives Hall 305
How:
Placement Exam (take home): due on September 2nd in class
Midterm Exam: 10/20/2015 STL185: Statler Hall 185-Aud
Final Exam: Dec. 16th, 9AM, BTN100EAST: Barton Hall 100 East-Main Floor
Teaching Assistants: (Please see Piazza)
Office Hours: Mondays 9-10am in Gates 410
What:
Piazza: https://piazza.com/class/icxgflcnpra3ko
Syllabus: Still in the works (here is a draft)
Prerequisites:
- Mathematical maturity and experience
- Students interested in preparing for the exam are advised to work through the first three weeks of Andrew Ng's online course on machine learning.
- Knowledge of Matlab. If you are unfamiliar with Matlab, please consider taking or work through the first few weeks of Andrew Ng's online course on machine learning. You may also want to look at these resources.
Objective:
The goal of this course is to give an introduction to the field of machine learning. The course will teach you basic skills to decide which learning algorithm to use for what problem, code up your own learning algorithm and evaluate and debug it.
Abstract:
The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience. Recently, many successful machine learning applications have been developed, ranging from data-mining programs that learn to detect fraudulent credit card transactions, to information-filtering systems that learn users' reading preferences, to autonomous vehicles that learn to drive. There have also been important advances in the theory and algorithms that form the foundation of this field. This course will provide a broad introduction to the field of machine learning. Prerequisites: CSE 241 and sufficient mathematical maturity (Matrix Algebra, probability theory / statistics, multivariate calculus). There is no enrollment limit, but the instructor will hold a take-home placement exam (on basic mathematical knowledge) that is due on the first day of class.
Topics:
This course will teach you (amongst other things):
- Parametric / Non-parametric learning
- Bias/Variance Trade-off
- Boosting
- Support Vector Machines
- Deep Learning
- Bayesian vs. frequentist learning
- Unsupervised learning
- Recommender systems
Course Books:
The main book is Kevin Murphy Machine Learning A Probabilistic Perspective. As reference book we will also use Hastie, Tibshirani, Friedman The Elements of Statistical Learning.
