CS 3750  Advanced Topics in Machine Learning (ISSP 3535)


Time:  Tuesday, Thursday 4:00pm-5:15pm 
Location: Sennott Square, Room 5313


Instructor:  Milos Hauskrecht
Computer Science Department
5329 Sennott Square
phone: x4-8845
e-mail: milos_at_cs_pitt_edu
office hours: by appointment


Announcements !!!!!



Links

Course description
Lectures
TBA
Paper presentations
Projects



Abstract

The goal of the field of machine learning is to build computer systems that learn from experience and that are capable to adapt to their environments. Learning techniques and methods developed by researchers in this field have been successfully applied to a variety of learning tasks in a broad range of areas, including, for example, text classification, gene discovery, financial forecasting, credit card fraud detection, collaborative filtering, design of adaptive web agents and others.

The objective of the Advances Machine Learning course is to expand on the material covered in the introductory Machine Learning course (CS2750), and focus on special topics in ML such as, latent variable and dimensionality reduction models, active, transfer, multidimensional learning, learning with multiple annotators, outlier detection. The course will consist of a mix of lectures, presentations and discussions. Students will be evaluated based on their participation in discussions, presentations and projects.

Prerequisites

CS 2750 Machine Learning , or the permission of the instructor.



Readings:

We will use readings from:

In addition we will use conference and journal paper readings that will be distributed electronically or in a hardcopy form.

Other (useful) books



Lectures
 
 
Lectures  Topic(s) 
August 26 Course Administration

Readings:

August 28 Review of CS 2750 material

Readings:

September 2 Markov Random Fields (MRFs)

Readings:

  • Bishop. Pattern Recognition and Machine Learning. Chapter 8.
September 4 Markov Random Fields (MRFs) II: inference, variable elimination, belief propagation

Readings:

  • Bishop. Pattern Recognition and Machine Learning. Chapter 8.
September 9 Markov Random Fields (MRFs) III: inference, learning

Readings:

September 11 Markov Random Fields (MRFs) IV: learning

Readings:

September 16 Monte Carlo methods

Readings:

September 18 Monte Carlo methods: MCMC

Readings:

September 23 PCA and SVD (Eric Strobl)

Readings:

Other resouces: A book chapter on SVD
September 25 Applications of SVD: Latent semantic analysis, Link analysis (Daniel Steinberg)

Readings:

Applications of PCA:

September 30 Latent Variable Models for text analysis, information retrieval and link analysis: PLSA, (Lingjia Deng)

Readings:

Probabilistic latent semantic analysis (pLSA)

pLSA for link analysis

October 2 Latent Dirichlet Allocation (LDA) (Mahdi Pakdaman)

Readings:

Latent Dirichlet Allocation

October 7 Probabilistic PCA, extensions (Milos)

Readings:

EM algorithms for probabilistic PCA

Extensions of probabilistic PCA

  • Wray Buntine and Sami Perttu. Is multinomial PCA multi-faceted clustering or dimensionality reduction AI in statistics, 2003.
  • Michael Collins, Sanjoy Dasgupta, Robert E. Schapire. A Generalization of Principal Component Analysis to the Exponential Family
  • October 9 Probabilistic models of time-series and sequences (Zitao)

    Readings:

    • Bishop. Chapter 13.
    October 16 Conditional Random Fields (Patrick Luo)

    Readings:

    October 21 Latent component analysis and variational methods. (Milos)

    Readings:

    October 23 Laplacian Eigenmaps for dimensionality reduction (Daniel Steinberg)

    Readings:

    October 28 Spectral clustering (Salim Malakouti)

    Readings:

    October 30 Label propagation on graphs. Semi-supervised learning (ChangSheng Liu )

    Readings:

    November 4 Metric, kernel learning (Eric Heim)

    Readings:

    November 6 Active Learning (Nils Murrugara Llerena)

    Readings:

    November 11 Multilabel learning (Charmgil Hong)

    Readings:

    November 13 Transfer learning (Jaromir Savelka)

    Readings:

    November 18 Learning from multiple annotators (Gaurav Trivedi))

    Readings:

    November 20 Zero and one shot learning (Jeya Balaji Balasubramanian))

    Readings:

    November 25 Deep learning, representation learning (Yoonjung Choi))

    Readings:

    Videolectures:

    Other readings:
    December 2 Outlier detection (Yanbing Xu)

    Readings:

    December 4 Compressed sensing (Ka Wai Yung, Huichao Xu)

    Readings:

    Videolectures:


    Course webpage for CS2750, the introductory Machine Learning course from Spring 2014. It is the prerequisite of CS3750.



    Readings

    Readings will be assigned before the class at which the discussion on the topic takes place. Most of the readings will be electronic, however, some readings will be in the paper form or from the books. See a summary list of Readings for different topics



    Paper discussions

    Every student is expected to be in charge of at least one topic, present it, and lead the discussion on the topic during the class. The readings for each topic will be distributed electronically. The assignment of the papers will be discussed during the first two week of the course.
     



    Projects

    There are no homeworks in this course. However, students will be asked to prepare, submit and present two projects. The first project will be assigned and due in the middle of the semester. The final project (due at the end of the semester) and is more flexible: a student can choose from a set of topics/problems or propose his/her own topic to investigate. If you plan to propose your own project/topic you will need to submit a short (one page) proposal for the purpose of approval and feedback. In general, the final project must have a distinctive and non-trivial learning or adaptive component.



    Last updated by milos on 08/26/2014