Course Name 
Intelligent Information Systems
Course Overview 
Level of study	Bachelor Degree	
Workload	ECTS: 3
Total Hours: 108
Contact Hours: 44
•	Lectures: 22
•	Labs: 11
•	Seminars: 11	
Course Code	B1.VM5.2.9	
Semester	summer	
Prerequisites	Database	
Course Objectives	In this course, you will explore dimensionality reduction techniques for modeling high-dimensional data and machine learning. In the case of recommender systems, your data is represented as user-product relationships, with potentially millions of users and hundred of thousands of products. You will implement matrix factorization and latent factor models for the task of predicting new user-product relationships. You will also use side information about products and users to improve predictions.	
Learning Outcomes
	Learning Outcomes: By the end of this course, you will be able to: 
-Identify potential applications of machine learning in practice. 
-Describe the core differences in analyses enabled by regression, classification, and clustering. 
-Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. 
-Represent your data as features to serve as input to machine learning models. 
-Assess the model quality in terms of relevant error metrics for each task. 
-Utilize a dataset to fit a model to analyze new data. 
-Build an end-to-end application that uses machine learning at its core.
-Create a collaborative filtering system. 
-Reduce dimensionality of data using SVD, PCA, and random projections. 
-Perform matrix factorization using coordinate descent. 
-Deploy latent factor models as a recommender system. 
-Handle the cold start problem using side information. 
-Examine a product recommendation application.	
Syllabus
	Module 1:  Machine Learning Foundations
1.	Introduction
2.	Microsoft Decision Trees
3.	Microsoft Clustering
4.	Microsoft Native Bayes
5.	Microsoft Time Series
6.	Microsoft Association
7.	Microsoft Sequence Clustering
8.	Microsoft Neural Network
9.	Microsoft Linear Regression
10.	Microsoft Logistic Regression
Module 2: Business Intelligence Concepts and Platform Capabilities
11.	Lesson 2.1: BI Concepts
12.	Lesson 2.2 : BI Platform Capabilities
13.	Lesson 2. 3: Business Reporting
14.	Lesson 2. 4: BI OLAP Styles
Module 3 - Data Visualization and Dashboard Design
15.	Lesson 3.1: Data Visualization
16.	Lesson 3. 2: Data Visualization Guidelines and Pitfalls
17.	Lesson 3. 3: Performance Dashboards
18.	Lesson 3. 4: Dashboard Design Guidelines and Pitfalls	
Labs
	1.	VS BI
2.	Microsoft SQL Server Analysis Services (SSAS)
•	Microsoft Decision Trees
•	Microsoft Clustering
•	Microsoft Native Bayes
•	Microsoft Time Series
•	Microsoft Association
•	Microsoft Sequence Clustering
•	Microsoft Neural Network
•	Microsoft Linear Regression
•	Microsoft Logistic Regression
3.	SQL reporting	
Projects	not provided	
Assessment	Credit Test ( Pass/Fail)	
Resources
	https://www.coursera.org/learn/ml-recommenders
https://www.coursera.org/specializations/machine-learning
https://www.coursera.org/learn/business-intelligence-tools	
Instructors	Ponomarev A.A.
http://portal.tpu.ru/SHARED/a/AAPONOMAREV/eng