Machine Learning for Intelligent Systems


CS4780/CS5780: Machine Learning for Intelligent Systems [FALL 2018]

(painting by Katherine Voor)

Attention!! You have to pass the (take home) Placement Exam in order to enroll.
(See Details below.)
It is a good idea to start the exam (ideally do it completely) over the winder break and brush up whatever topics you feel weak at.

This year we will use
python-logo@2x as our main programming languages.

Kilian Weinberger

Tu/Thu 11:40am
Statler Auditorium

Placement Exam (take home): due 3rd lecture in class, due noon [11:59am] - must be submitted through Gradescope ( enrollment code MK3R5N)
Midterm Exam: Oct. 16 (see Piazza for details)
Final Exam: Dec. Wed, Dec 12, 2:00 PM
Office Hours (with Kilian): Please see TA office hours first, then if problem remains unresolved make an appointment here ( )
Teaching Assistants: see piazza
Piazza page:

Youtube Video Lectures:
Lecture Notes:

- Mathematical maturity and experience
- Students interested in preparing for the placement exam ahead of class are advised to work through the first three weeks of Andrew Ng's online course on machine learning.

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.

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). The instructor will hold a take-home placement exam (on basic mathematical knowledge) that is due on January 30th.

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.

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