Machine
Learning is the study of how to build computer systems that
learn from experience. It is a subfield of Artificial Intelligence and
intersects with statistics, cognitive science, information theory, and
probability
theory, among others. The course will explain how to build systems that
learn
and adapt using real-world applications from industry and science
(e.g.,
learning to classify astronomical objects, to predict medical
diagnoses, to
play chess, etc.).
Administrative
Details
Here
you will find administrative information for the Winter 1397.
.
Instructor: Dr. Mohsen Afsharchi, afsharchi at znu.ac.ir
.
Lectures: Sat 11-12:30 Tue 8-9:30
.
Office
Hours: Wednesday and Saturday
.
Prerequisites:
Clear understanding of
probability, common data structures, algorithms, standard programming
and
preferably some preliminary AI concepts.
The
required textbooks
for this course are:
.
Some
supplementary
textbooks (recommended, but not required) are:
.
Machine Learning Materials, by Andrew NG, Available
Online
.
Machine Learning A Probabilistic Perspective, by Kevin Murphy, Available
Online
.
Artificial
Intelligence: A Modern Approach
(Third Edition), by Stuart J. Russell, Peter Norvig, Prentice Hall
2010 .
Lecture
material will
be drawn from textbooks, as well as from some of the recent online
materials.
Course
Syllabus and Lecture Notes
- Foundations
of Machine Learning: Introduction(PDF),
A good Introduction By Rob Schapire (PDF)
- Simple Learning Algorithms: Decision Trees(PDF), K-nearest neighbors(PDF)
-
Supervised Learning: Linear Regression (PDF) Courtesy of Andrew NG
, Classification and Logistic Regression (PDF) (Logistic Regression Python code)(Sample data set)
- Generative Learning Algorithms: Multivariate Normal Distribution (PDF), Gaussian Discriminant Analysis (PDF)
- Neural Networks: Perceptron (PDF), Backpropagation (PDF)
- Deep Learning: Convolutional Networks (PDF), A Simple Example (PDF) Courtesy of Brandon Rohrer
- Unsupervised Learning: Clustering (PDF), K-means Algorithm (PDF)
- Mixture Models: Mixture of Gaussians (PDF), Expectation Maximization Algorithm (PDF)
- Markov
Processes: Markov Models, Hidden Markov Models(PDF)
- Markov Models:
Applications:
(PDF, Courtesy of Jan Rupnic
)(Markov Chain Interpretation of Google Page Rank, PDF ,PDF)
- Hidden Markov
Models:
(Particle Filtering, PDF)
- Markov Decision
Process: (PDF) Reading Material (Scalable MDP Based Planning...)
- Reinforcement
Learning:
(PDF)
- Large Margin Classifiers: Support Vector Machines (PDF), Kernels (PDF) Courtesy of Andrew NG
Course Work
and Evaluation
- HomeWork 1: Linear Regression and locally weighted linear regression (PDF) (Simple Python GD code)
- HomeWork 2: CNN for Digit Recognition (PDF)
- HomeWork 2: Neural Networks (PDF)
- HomeWork 3: Linear Regression and EM (PDF)
- HomeWork 4: Mixture of Gaussians for Classification and Non-linear Regression (PDF) (Data set a , JPG) (Data set b , JPG)
Possibly
Interesting URLs
Here
is an ad hoc
collection of relevant ML links and interesting tidbits. If you know of
other good
stuff to share with your classmates here, please let me know, and I
will try to
add it.
- Machine
Learning Thoughts
- Index
of Machine Learning Courses
- Another
Index of Machine Learning Courses
- Clustering
in Google News Personalization