Machine Learning Workshop
This wokshop spans 64 hours over 4 weeks.
Two times per week ,(Saturdays&Tuesdays) each session 4 hours from 8 am t o 12 pm. Date: Jul 15/79/8/18 Location: iHub Studio, Ain Shams University CHEP Training Credit: 4 weeks Cost: Two Credit Hours 
Instructor
Eng. Aly Osama

Registration Status

OVERVIEW
This workshop targets 3rd and 4th year students from computer science/computer Engineering,
It gives overview on main machine learning algorithms families and how to use each algorithm.
GOALS
This workshop targets 3rd and 4th year students from computer science/computer Engineering,
It gives overview on main machine learning algorithms families and how to use each algorithm.
GOALS
 Students know the main branches of Machine learning Algorithms Tree.
 Learn when and how to use each algorithm and its pros and cons.
 Practice on each algorithm even on a simple dataset to gain the experience of using the algorithms and see its results on the given data.
 Get familiar with using R scripts.
 Learn how to evaluate each algorithm result.
 Know where they can find data resources online.
 Learn how to make feature selection from the given data
COURSE CONTENT
Sat,5 th August
● Introduction about Machine Learning and Data science ● Review Main basics in Linear Algebra (needed in this course) ● Linear Classifiers ( Discriminant Function Classifiers ) 
[Practical]
➢ Getting started with R scripts ○ Load data and read it in vars ➢ Apply Least Square Classifier on given Data ➢ Apply Fisher Classifier on given Data 
Tues, 8 th August
● Review on main basics in Probability (needed in this Session) ● Linear Classifiers ( Probabilistic Models 
[Practical]
➢ Apply Naive Bayes Classifier on given Data ➢ Apply Logistic Regression 
Sat, 12 nd August
● Non Linear Classifiers ○ Knn ○ Weighted knn ○ Decision Tree 
[Practical]
➢ Apply Knn Classifier on given Data ➢ Apply weighted Knn Classifier on given Data ➢ Apply Decision Tree Classifier on given Data 
Tues, 15 th August
● Clustering Techniques ○ Kmeans ○ Kmedoid ○ How to choose best K 
[Practical]
➢ Apply Kmeans on given Data ➢ Apply Kmedoid on given Data 
Sat, 19 th August
● Clustering Techniques ○ Hierarchical Clustering ○ Specular Clustering 
[Practical]
➢ Apply Hierarchical Clustering technique on given Data ➢ Apply Specular Clustering technique on given Data 
Tues, 22 nd August
● Feature Selection ○ Ttest Method ○ Forward and backward wrapper method 
[Practical]
➢ Apply ”Ttest” method for feature selection on “ISOLTE” Dataset 
Sat, 26 th August
● Principal Component Analysis ○ Linear PCA ○ Nonlinear PCA 
[Practical]
➢ Apply ”Linear PCA” to do feature transformation ➢ Apply ”Nonlinear PCA” to do feature transformation 
Tues, 29 th August
● Support vector machine ○ Linear SVM ○ Nonlinear SVM 
[Practical]
➢ Apply ”Linear SVM” on given data. ➢ Apply ”Nonlinear SVM” on given data. 
Thurs,31 st August
● Ensemble Learning ○ Bagging ○ Boosting ○ Random Forest Technique 
[Practical]
➢ Apply ”Bagging” , “Boosting” and ”Random forest” on MNIST Dataset 
Sat, 9 th Sep
● Neural Network ○ Build small networks ○ How to build complex Network ○ Into to Deep Learning ○ Resources to Deep learning 
[Practical]
➢ Apply Neural Network on given Data. 
Tues, 12 nd Sep
● Text Mining Session (set by another Research assistant )
● Text Mining Session (set by another Research assistant )