1. Machine Learning in CSC 196K
What motivated machine learning?
What is machine Learning?
Statistics and ML algorithms design

2. 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.

3. 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:
Machine
Learning
algorithm
Mapping
function
Training
set

4. 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)

5. 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

6. 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