Prerequisite 
Basic probability and statistics 
Instructor 
Dr. Ghofraniha, Jahan 
Starting Date 
3/24/2018 
Complete Date 
5/12/2018 
Lecturing time 
Tuesday 7:30 PM to 9:30 PM 
Sunday 1:30 pm to 5:30 PM 
Place 
1601 McCarthy Blvd., Milpitas, CA 95035 
Contact 
info@cstu.org 
A. COURSE DESCRIPTION This course introduces methods and techniques for using stored data to make decisions. The student will learn data exploration and analysis and learn their patterns, associations, or relationships, and how to use these information for decision making. Fundamentals of Machine Learning such as regression, classification, decision trees, model reduction techniques such as principle component analysis, ensemble learning will be introduced. Specific examples of engineering and businesses using Machine Learning techniques will be given in the course.
The student is required to work on a course projects by using modern data analysis software and cases studies. This course will focus on implementation of ML algorithms using Python and Scikitlearn libraries.
B. COURSE OBJECTIVES

To learn how computational procedures and techniques are employed in machine learning.

To provide insights into the implementation details of machine learning strategies.

To gain handson experience with machine learning tools.
Textbook: An Introduction to Statistical Learning with Applications in R
 Series: Springer Texts in Statistics (Book 103)
 Hardcover: 426 pages
 Publisher: Springer; 1st ed. 2013, Corr. 5th printing 2015 edition (August 12, 2013)
 Language: English
 ISBN10: 1461471370
 ISBN13: 9781461471370
 Handson Machine Learning with ScikitLearn and TensorFlow
 Paperback: 576 pages
 Publisher: O'Reilly Media; 1 edition (April 9, 2017)
 Language: English
 ISBN10: 1491962291
 ISBN13: 9781491962299
Week 1: Statistical Learning and machine learning overview
 Supervised vs. unsupervised learning
 Assessing model accuracy
 Introduction to Python and ML libraries
 Dealing with data
 Data visualization
 Data cleaning
 Selecting and training a model
 Basic exercise and homework using Python
Week 2: Classification
 Binary classification
 Multiclass classification
 Error analysis
 Cross validation
 Measuring accuracy using crossvalidation
 Precision/Recall tradeoff
 Inclass exercise
 Exercise/homework in Python and scikitlearn library
Week 3: Regression and other linear and quasilinear model training
 Linear regression
 Computational complexity
 Gradient Descent
 Batch gradient descent
 Stochastic gradient descent
 Polynomial regression
 Learning curves
 Regularization
 Logistic regression
Hw: Midterm project announcement and discussion
Week 3: Treebased Methods
 Basis decision trees
 Classification decision trees
 Regression decision trees
Hw: Application of decision trees in classification and regression using Python and Scikitlearn library
Week 4: Support Vector Machines
 Mathematical background, concept of hyperplane in ndimension
 SVM classifier
 SVM with nonlinear decision boundaries
 SVM with more than two classes
 SVM example in Python and Scikitlearn
Hw: Using SVM for classification and regression in Python and Midterm project
Week 5: Ensemble learning and Random Forests
 Voting classifier
 Bagging and Pasting in Scikitlearn
 Outofbag Evaluation
 Random Forests
 Feature importance
 Boosting
 AdaBoost
 Gradient Boosting
 Stacking
Hw: Comparison of Random forests vs boosting and voting classifier in Python
Week 6: Dimensionality reduction
 Main Approaches for Dimensionality Reduction
 Projection
 Manifold learning
 PCA
 Preserving variance
 PCA for compression
 Incremental PCA
 Randomized PCA
 Kernel PCA
 Other techniques
Hw: PCA exercise using scikitlearn and final project announcement
Week 7: Introduction to Artificial Neural Networks
 From biology to Artificial Neurons
 The perceptron
 Multilayer Perceptron and backpropagation
 Number of hidden layers
 Number of neurons per layer
 Activation function
 Implementation using Python
Week 8: Unsupervised Learning and final project presentations
 Clustering methods
 Kmean clustering
 Hierarchical clustering • Selforganizing Map
 Kohonen SOM
 SOM example
Hw: Final project presentation and Final exam