Machine Learning

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.

Textbooks

The required textbooks for this course are:

.      

Pattern Recognition and Machine Learning

Christopher M. Bishop, Springer (2006)

 
  

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

  1. Foundations of Machine Learning:  Introduction(PDF), A good Introduction By Rob Schapire (PDF) 
  2. Simple Learning Algorithms: Decision Trees(PDF), K-nearest neighbors(PDF)
  3. Supervised Learning: Linear Regression (PDF) Courtesy of Andrew NG , Classification and Logistic Regression (PDF) (Logistic Regression Python code)(Sample data set)
  4. Generative Learning Algorithms: Multivariate Normal Distribution (PDF), Gaussian Discriminant Analysis (PDF)
  5. Neural Networks: Perceptron (PDF), Backpropagation (PDF)
  6. Deep Learning: Convolutional Networks (PDF), A Simple Example (PDF) Courtesy of Brandon Rohrer
  7. Unsupervised Learning: Clustering (PDF), K-means Algorithm (PDF)
  8. Mixture Models: Mixture of Gaussians (PDF), Expectation Maximization Algorithm (PDF)
  9. Markov Processes:  Markov Models, Hidden Markov Models(PDF)
  10. Markov Models: Applications: (PDF, Courtesy of  Jan Rupnic )(Markov Chain Interpretation of Google Page Rank, PDF ,PDF)
  11. Hidden Markov Models: (Particle Filtering, PDF)
  12. Markov Decision Process: (PDF) Reading Material (Scalable MDP Based Planning...)
  13. Reinforcement Learning: (PDF)
  14. Large Margin Classifiers: Support Vector Machines (PDF), Kernels (PDF) Courtesy of Andrew NG

 

 

 

Course Work and Evaluation

  1. HomeWork 1: Linear Regression and locally weighted linear regression (PDF) (Simple Python GD code)
  2. HomeWork 2: CNN for Digit Recognition (PDF)
  3. HomeWork 2: Neural Networks (PDF)
  4. HomeWork 3: Linear Regression and EM (PDF)
  5. HomeWork 4: Mixture of Gaussians for Classification  and Non-linear Regression (PDF) (Data set a , JPG) (Data set b , JPG)
  6.       

    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.

    1. Machine Learning Thoughts
    2. Index of Machine Learning Courses
    3. Another Index of Machine Learning Courses
    4. Clustering in Google News Personalization