Machine Learning


CS4780/CS5780: Machine Learning [Spring 2017]

Attention!! You have to pass the (take home) Placement Exam in order to enroll.
Please bring it with you to the second lecture of the semester.
It is a good idea to start with the exam over the winder break and brush up whatever topics you feel weak at.

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

Associate Prof. Kilian Weinberger

Mo/We/Fr 1:25pm-2:15pm
Klarman Hall KG70

Placement Exam (take home): due 2nd lecture in class
Midterm Exam: 3/21/2017 STL185
Final Exam: Dec. 5/17/2017 STL 185 9am
Office Hours: Mondays 9-10am Gates 410
Teaching Assistants: See Piazza
Piazza page:
Video Lectures:

- Overview (What is machine learning)
- k-nearest neighbors
- The Perceptron
- Estimating Probabilities from Data
- Naive Bayes
- Logistic Regression
- Gradient Descent
- Linear Regression
- Linear SVM
- Empirical Risk Minimization
- Bias / Variance Tradeoff
- ML Debugging, Over- / Underfitting
- Kernel Machines I
- Kernel Machines II
- Gaussian Processes / Bayesian Global Optimization
- Fast nearest neighbor search
- Decision / Regression Trees
- Bagging
- Boosting
- Deep Learning

- Homework 1 (solutions)
- Homework 2 (solutions)
- Homework 3 (solutions)
- Homework 4 (solutions)
- Homework 5 (solutions)
- Homework 6 (solutions)

- 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.

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). 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.

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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