CS 3750  Advanced Topics in Machine Learning (ISSP 3535)


Time:  Tuesday, Thursday 11:00pam-12:15pm 
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). It focuses on special topics in ML such as exact and approximate inference in graphical models, dimensionality reduction and component analysis methods, latent variable models, models of documents and words, time series models and selected topic from deep neural networks. The course will consist of (student-lead) 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:

In the first part of the course 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.



Lectures
 
 
Lectures  Topic(s) 
August 28 Course Administration

Readings:

August 30 Graphical models: Bayesian belief networks, MRFs, conversions

Readings:

  • Bishop. Pattern Recognition and Machine Learning. Chapter 8.
September 4 Graphical models: inference on chains and factor graphs

Readings:

  • Bishop. Pattern Recognition and Machine Learning. Chapter 8.
September 6 Graphical models: inference on clique trees

Readings:

September 11 Monte Carlo methods: sampling BBNs, rejection sampling, importance sampling

Readings:

September 13 Monte Carlo methods: MCMC
Variational methods

Readings:

September 18 ( PCA, Autoencoders (presenter Xiaozhong Zhang)

Readings:

  • Chris Bishop. Chapter 12.1.
  • Tutorial on PCA by J. Shlens
  • Tutorial on PCA by L. Smith
  • Chapter 14. In: Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning.
  • September 20 Latent variable models (presenter Cui Tianyi)

    Readings:

    September 25 Non-linear dimensionality reduction and kernels: eigenmaps, isomaps, locally linear embeddings (presenter: Hanzhong Zheng)

    Readings:

    September 27 Singular value decomposition, applications of SVD, non-negative matrix factorization (presenter: Sumendha Singla)

    Readings:

    October 2 Document and topic models: pLSA, LDA (presenters: Andrew Levandoski and Jonathan Lobo)

    Readings:

    October 4 Word similarity and word models: word2vec, CBOW, graph-based similarities (presenter: Ahmed Magooda)

    Readings:

    October 9 Gaussian Processes: basics, regression (presenter: Zhenjiang Fan)

    Readings:

    October 11 Gaussian Processes: classification (presenter: Jinpeng Zhou)

    Readings:

    October 18 Sequences and Time series: Markov models, Dynamic Belief networks, Hidden Markov models (presenter: Yanbing Xue)

    Readings:

    • Chris Bishop. Pattern Recognition and Machine Learning. Chapter 13.1-2.
    • Russell and Norvig. Artificial Intelligence. Modern Approach. Chapter 15. Probabilistic reasoning over time.
    October 23 Time Series: Autoregressive models (first and k-order AR, ARMA, ARIMA)(presenter: Mingda Zhang)

    Readings:

    October 25 Sequences and Time series: Linear dynamical model (presenter: Matt Barren)

    Readings:

    • Chris Bishop. Pattern Recognition and Machine Learning. Chapter 13.3.
    • Russell and Norvig. Artificial Intelligence. Modern Approach. Chapter 15.4. Probabilistic reasoning over time.
    October 30, 2018 Sequences and Time series: Particle filters (presenter: Ekaterina Dimitrova)

    Readings:

    November 1, 2018 Sequences and Time series: Continuous-time models (presenter: Siqi Liu)

    Readings:

    November 6, 2018 Project presentations

    Readings:

    • Lectures from September 27, October 2, October 4 on SVD, pLSA, LDA, word models.
    November 8, 2018 Sequences/Deep Neural Nets: Recurrent neural networks, Long-short term memory (LSTM) (presenter: Jeongmin Lee)

    Readings:

    November 13, 2018 Sequences/Deep Neural Nets: Convolutional neural networks (presenter: Yu Ke)

    Readings:

    November 15, 2018 Deep Neural Nets: Deep generative models (presenter: Hung Chau)

    Readings:


    Course webpage for CS2750, the introductory Machine Learning course from Spring 2018. 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. The students should always read the book chapter and paper assigned and be prepared for the class. Short randomly-scheduled quizes can be administered to recheck if students follow the readings.

    See the list of readings for different topics (currently incomplete)



    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 weeks of the course.
     



    Projects

    There are two projects for course. The first project (a group 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 08/28/2018