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35-hr course introducing theoretical foundation of machine learning algorithms & practical applications of machine learning techniques in R.
Saturday, June 10, 2017 at 10:00 AM    Cost: $2990
NYC Data Science Academy, 500 8th Ave, Ste 905
 
     
 
 
              

        
 
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LOCATION
 
DESCRIPTION
This 35-hour course introduces both the theoretical foundation of machine learning algorithms as well as their practical applications of machine learning techniques in R. It will introduce you to data mining, performance measures & dimension reduction, regression models, both linear & generalized, KNN & Nave Bayes models, tree models, & SVMs as well as the Association Rule for analysis. After successfully completing of this course, you will be able to break down the mathematics behind major machine learning algorithms, explain the principles of machine learning algorithms, & implement these methods to solve real-world problems.

Prerequisites

Knowledge of R programming
Able to munge, analyze, & visualize data in R


Syllabus

Unit 1: Foundations of Statistics & Simple Linear Regression

Undestand your data
Statistical inference
Introduction to machine learning
Simple linear regression
Diagnostics & transformations
The coefficient of determination

Unit 2: Multiple Linear Regression & Generalized Linear Model

Multiple linear regression
Assumptions & diagnostics
Extending model flexibility
Generalized linear models
Logistic regression
Maximum likelihood estimation
Model interpretation
Assessing model fit

Unit 3: kNN & Naive Bayes, the Curse of Dimensionality

The K-Nearest Neighbors Algorithm
The choice of K & distance measure
Conditional probability: Bayes Theorem
The Naive Bayes Algorithm
The Laplace estimator
Dimension reduction
The PCA procedure
Ridge & Lasso regression
Cross-validation

Unit 4: Tree Models & SVMs

Decision trees
Bagging
Random forests
Boosting
Variable Importance
Hyperplanes & maximal margin classifier
Sort margin & support vector classifier
Kernels & support vector machines

Unit 5: Cluster Analysis & Neural Networks

Cluster analysis
K-means clustering
Hierarchical clustering
Neural networks & perceptrons
Sigmoid neurons
Network topology & hidden features
Back propagation learning with gradient descent

Final Project

After 35 hours of structured lectures, students are encouraged to work on an exploratory data analysis project based on their own interests. A project presentation demo will be arranged afterwards.
 
 
 
 
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