Machine Learning
Rationale
The rapid growth of computer power and the needs for information technology have made Machine Learning an essential part of systems that must interpret data by classifying or clustering. This course gives a a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning.
Description
Machine learning is a branch of articial intelligence, concerned with the construction and study of systems that can learn from data. Data may be numeric or symbolic and typically has the form of an Ntuple. The anthropomorphic term learning in the machine learning context means being able to predict some unobserved components of an Ntuple given some observed components of the Ntuple. This course provides a detailed explanation of many of the techniques used in machine learning and statistical pattern recognition.
Prerequisites:
Calculus
Linear Algebra
Probability
Statistics
Discrete Mathematics
Use of set builder notation
Fluency in one of the following programming languages:
C, C++, Python
Topic List
Topics may include but are not limited to:
Bayesian Classication
Class conditional probabilities
Prior Probabilities
Gain Matrix
Maximizing Expected Gain
Minimax Classication
Parametric Probability Models
NonParametric Probability Models
Making Decisions in Context
Conditional Independence
Hidden Markov Models
Forward Backward Algorithm
Graphical Models
Semigraphoids
Graphoids
Bayesian Nets
Decision Trees
Nearest Neighbor
Linear Regression
Logistic Regression
Principal Component Analysis
Neural Networks
The Perceptron Algorithm
The Back Propagation Algorithm
Deep Learning
Linear Decision Rules
Fisher Linear Decision Rule
Support Vector Machines
Kernel Methods
Ensemble Learning
Evolutionary Learning
Clustering
KMeans Clustering
Expectation Maximization
Linear Manifold Clustering
Gaussian Mixture Models
Clustering Evaluation Measures
Experimental Protocols
Training Sets
Test Sets
CrossValidation
Performance Characterization
Learning Goals
The student must be able to demonstrate a working knowledge of the theoretical foundations and software of machine learning represented by the topics of:

Bayesian Classication

Nonparametric Probability Models

Clustering

Dimensionality Reduction

Performance Characterization
Assessment
Written exams and course projects will be assigned to make sure students are capable of identifying suitable algorithms for making certain types of predictions, designing experimental protocols to evaluate the performance of those proposed algorithms, and implement experiments on the algorithms and evaluations. 40% Important machine learning knowledge to be assessed by a final project includes but not limited to: Classication, Regression, Clustering, Dimensionality Reduction, and Performance Characterization. 60%