Machine Learning in
CSC 196K
What motivated machine
learning?
What is machine Learning?
Statistics and ML algorithms
design
Machine learning is motivated
by a class of problems
No traditional algorithmic solution
Computationally expensive
Traditional algorithm
Input => algorithm => Output
When algorithm=?, we may use machine
learning algorithm to construct a
mapping function between input and
output.
Input and Output for Machine
Learning Algorithm
For the class of ML algorithms that
generalization performance is based on
a given finite number of training
examples, we have:
Training
set
Mapping
function
Machine
Learning
algorithm
Statistics and ML algorithms
design
ML
methods overcome shortcomings of
statistical methods (estimation, classification,
prediction)
Learning bias
–
prior assumption of a
Bayesian
Nonlinear
problems

regression, linear
model
Learning systems that use a
hypothesis
space

hypothesis testing
Statistical learning
theory (SVM

Support
Vector Machine)
Types of Data mining
Techniques in CSC 196K
Techniques for data mining covered in
csc196K
Data warehousing
–
OLAP and data preprocessing
Statistics
Machine learning
This course only covers data mining related
ML techniques and concepts (a subset of ML
methods)
Data mining is one of many applications of
machine learning
To Learn more on Machine
Learning and Applications
CSC 219 Machine Learning
CSC 215 Artificial Intelligence
CSC 196L Intelligent Systems
Publications in CSUS Library
On

line info
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