CptS 570 Fall 2016: Machine Learning

  Basic Information

Instructor: Janardhan Rao (Jana) Doppa
Email: jana AT eecs dot wsu dot edu
Office: EME 133
Office hours: Mon, Fri 4-5
Class email-list: We will use piazza for all the class announcements and discussions.

  Quick Links

[ Announcements ]    [ Course Information ]     [  Lecture Schedule ]     [ Homeworks and Exams ]     [ Textbooks and Additional Resources  ]

  Announcements

  Course Information

Contents of the Course:

This course will provide an introduction to machine learning algorithms and the fundamental concepts related to learning. These algorithms are widely used in several real-world applications including spam filters (classifying an email as spam or not spam); optical character recognition (reading postal addresses); face recognition (recognizing people from their images); speech recognition and synthesis systems; and search engines (ranking webpages based on keyword queries).  Every machine learning algorithm has both a computational aspect (how to compute the answer) and a statistical aspect (how to ensure that future predictions are accurate), and there is a strong interaction between the computational and statistical issues.

A *tentative* list of the topics that will be covered in this course are as follows:

    1) Introduction to different learning paradigms: supervised learning, semi-supervised learning, unsupervised learning, active learning, reinforcement learning
   
    2) Supervised Learning: Learning a mapping from input-output pairs of training examples (classification for discrete outputs, regression for continuous outputs, and ranking for preferences)   
       
    2.1: Online learning -- general design principles and derivation of different online learners (perceptron and passive-aggressive algorithms); Optional (confidence-weighted, and exponentiated gradient algorithms)
   
    2.2: Support Vector Machines (SVMs) and Kernel methods
          
    2.3: Naive Bayes and Logistic Regression classifiers
        
    2.4: k-Nearest Neighbor (kNN); Decision tree classifiers; and Regression trees 
  
    2.5 Over-fitting and Under-computing in machine learning; Bias/Variance theory
   
    2.6 Ensemble methods -- bagging and boosting
   
    2.7 Approaches to prevent over-fitting -- penalty methods, cross-validation, and ensembles
   
    2.8 Statistical and computational learning theory
   
    Additional Topics:
   
    2.8 Functional Gradient Boosting and variants
   
    2.9 Ranking: Algorithms and Applications
   
    2.10 Bayesian Optimization with applications to hyper-parameter tuning of machine learning algorithms
   
    3) Unsupervised Learning
   
    K-Means clustering
    Mixture of Gaussians
    Dimensionality Reduction
   
    4) Reinforcement Learning
   
    Markov Decision Processes (MDPs) Formalism
    Different RL settings
    Basic ideas behind policy iteration and value iteration
    Q-learning without and with function approximation 
    Classifier-based Policy Iteration (or Approximate Policy Iteration) for large-scale MDPs
       
    5) Active Learning
   
    Problem Setting
    Couple of popular algorithms

Learning Objectives of the Course:


Course Policies:


Grading Policy:
Late Policy:

All assignments and project proposal/report are due at the start of the class.  The late policy is as follows.
If you are late, please slip the assignment  through my office door.

Exam Policy:
Collaboration Policy:

Course Pre-requisites:


Safety on Campus:

 
Accommodation for Students with Disabilities:


   Lecture Schedule


Date Topic Suggested / Optional reading
Tue 8/23
Thu 8/25
Introduction to Machine Learning
Different learning paradigms: supervised, semi-supervised, unsupervised, active, and reinforcement learning


  Homeworks and Exams

I will try to return all the homeworks and exams within a week after submission. The rationale is to give you feedback as soon as possible, which is very important for learning.

  Textbooks and Additional Resources

We will not follow any fixed textbook for this course.  The instructor will provide the lecture slides and notes at the begining of each class.

An optional list of textbooks is as follows:
A list of additional resources that you may find useful:
A list of machine learning software that you can use as needed:
I will add more links to additional software over time.