CSC 290 : Introduction to Artificial Intelligence, Smith College, Department of Computer Science, Spring 2017

CSC290: Introduction to Artificial Intelligence

Classes: Monday – Wednesday 2.40 – 4.00 pm

Location: Ford Hall 342

Instructor: Dr. Şadi Evren ŞEKER (Office: Ford Hall 252)

Office Hours

  • Tuesday, 13.00 – 15.00
  • Other times by appointment/as available
  • Lunch meetings available by request for small groups

E-Mail: ai@sadievrenseker.com

Web Site: http://sadievrenseker.com/wp/?p=1172

Course Content:

  • History and Philosophy of the Artificial Intelligence (AI)
  • Classical AI approaches like search problems, machine learning, constraint satisfaction, graphical models, logic etc.
  • Learning how to model a complex real-world problem by the classical AI approach

Objectives:

  • Introduction to Artificial Intelligence Problems
  • Programming with a mathematical notation language (like a lisp variant, scheme)
  • Writing a real world application with an AI module (like a game)
  • Introducing sub-AI topics like neural computing, uncertainity and bayesian networks, concept of learning (supervised / unsupervised) etc.

Texts:

  • S. Russell and P. Norvig Artificial Intelligence: A Modern Approach Prentice Hall
  • —A must check : http://aima.cs.berkeley.edu
  • Some parts of the course is related to Machine Learning, Data Science, Data Mining, Pattern Recognition, Natural Language Processing, Statistics, Logic, Artificial Neural Networks and Fuzzy Logic, so you can read any [text] books about the topics.

—Grading: Programming assignment / Homeworks (30%), —Midterm Exam (20%), Final Exam (50%)

—Midterm and Final Exams (take home for 24 hours)

Course Outline:

  • —Introduction and Agents (chapters 1,2)
  • —Search (chapters 3,4,5,6)
  • —Logic (chapters 7,8,9)
  • —Planning (chapters 11,12)
  • —Uncertainty (chapters 13,14)
  • —Learning (chapters 18,20)
  • —Natural Language Processing (chapter 22,23)

Schedule and Contents (Very Very Very Tentative):

  • Class 1, Jan 30 : Introduction : Course Demonstration Slides, Introduction Slides
  • Class 2, Feb 1: Agents
  • Class 3, Feb 6:  Search
  • Class 4, Feb 8: Introduction to Scheme 1, Search Homework 1 (Due Date: TBA)
  • Class 5, Feb 13: Heuristic Search
  • Class 6, Feb 15:  Scheme Practice 2, Heuristic Homework 2  (Due Date: TBA)
  • Class 7, Feb 20: Constraint Satisfaction Problems
  • Class 8, Feb 22: Scheme Practice 3, CSP Homework 3  (Due Date: TBA)
  • Class 9, Feb 27: Game Playing
  • Class 10, Mar 1: Scheme Practice 4, Game Homework 4  (Due Date: TBA)
  • Class 11, Mar 6: Midterm
  • Class 12, Mar 8: Midterm Solutions
  • Mar 13, 15: No Classes , Spring Recess
  • Class 10, Mar 20:  Logic
  • Class 11, Mar 22: First Order Logic
  • Class 12, Mar 27: Inference in First Order Logic
  • Class 13, Mar 29: Scheme Practice 5, Logic Homework 5  (Due Date: TBA)
  • Class 14, Apr 3: Uncertainity and Fuzzy Logic
  • Class 15, Apr 5: Machine Learning and Problems
  • Class 16, Apr 10: Supervised / Unsupervised Learning and Classification / Clustering Problems, k-nn and k-means
  • Class 17, Apr 12: Naive Bayes, Decision Trees, Rule Based Learning, Error Calculation
  • Class 18, Apr 17: Scheme Practice 6, ML Homework 6  (Due Date: TBA)
  • Class 19, Apr 19: Prediction, Regression and Association Rule Mining
  • Class 20, Apr 24: Artificial Neural Networks
  • Class 21, Apr 26: Natural Language Processing
  • Class 22, May 1: Final Exam
  • Class 23, May 3: Final Exam Solutions

Collaboration Policy: You may freely use internet resources and your course notes in completing assignments and quizzes for this course. You may not consult any person other than the professor when completing quizzes or exams. (Clarifying questions should be directed to the professor.) On assignments you may collaborate with others in the course, so long as you personally prepare the materials submitted under your name, and they accurately reflect your understanding of the topic. Any collaborations should be indicated by a note submitted with the assignment.

Announcements

Please fill the knowledge card attached here, and send it back via email.

CSC102- 01: HOW THE INTERNET WORKS, Smith College, Department of Computer Science, Spring 2017

CSC102- 01: HOW THE INTERNET WORKS 

Classes: Tuesday- Thursday 10.30 – 11.50

Location: Ford Hall 342

Instructor: Dr. Şadi Evren ŞEKER (Office: Ford Hall 252)

Office Hours

  • Tuesday, 13.00 – 15.00
  • Other times by appointment/as available
  • Lunch meetings available by request for small groups

E-Mail: htiw@sadievrenseker.com

Web Site: http://sadievrenseker.com/wp/?page_id=631

Introduction: The Internet has transformed society, opening up communication channels never dreamed of by previous generations. This course introduces students to the structure, design, and operation of the Internet, beginning with the electronic and physical construction of networks and basic network protocols. It addresses personal safety online, how email and Web browsers work, and the design of simple Web pages. Along the way it explores the historical and societal implications of this new medium.

Texts:

Optional / also recommended (on reserve in Young library):

  • How The Internet Works, by Preston Gralla: Contains detailed labeled diagrams of many internet-related topics.
  • Basics of Web Design by Terry Felke-Morris: Contains detailed information on web page design and specific aspects of HTML, aimed at beginners. The more advanced portions of this book will also be useful if you take CSC 105.
  • Web 101, by Wendy Lehnert and Richard Kopec, which was used for this course in previous semesters.

In addition to the texts above, Wikipedia usually contains accurate information focused on specific topics relating to the Internet.

Tentative Course Outline

Class Topic Readings
I2N LWD P&IS HtIW BoWD W101
[PPT] Jan 26: Physics of Networks: SignalsFiber optics Ch. 1-3 ~ ~ Ch.1-2 ~ 1.1-1.4
2

[PPT1]

[PPT2]

Jan 31: History of InternetLANIP Ch. 4-5 ~ ~ Ch 3-4 ~ 1.5-1.6
3

[PPT]

Feb 2: Communications Protocols:  TCPDNS Ch. 6 ~ ~ Ch. 5-6 ~ ~
4

[PPT]

Feb 7: WWW Intro; HTTPURL Ch. 7 Ch. 1-2 ~ Ch. 17-18 Ch. 1 & 4 1.7-1.8
5

[PPT]

Feb 9: HTMLintroduction ~ 4-6 ~ Ch. 19-20 Ch. 2-3 9.1-9.3
[PPT] Feb 14: HTML: tablesrelative URL, etc. ~ 7-8 ~ Ch. 21 Ch. 9 10.1-10.2
[PPT] Feb 16: Color & ImagesForms ~ Ch. 9, 21 ~ Ch. 22 Ch. 6 & 10 9.6, 10.5
[PPT] Feb 21: Style rules; Multimedia ~ 10-13 ~ ~ Ch. 5 & 7 & 11 10.3-10.4
[PPT] Feb 23: EmailSMTPspam ~ ~ Ch. 13 Ch. 11-12 ~ 3.1-3.6
10 [PPT] Feb 28: Personal Safety: cookiesphishing, etc. ~ ~ Ch. 4 & 6 Ch. 44-49 ~ 2.1-2.16
11

[PPT]

Mar 2: Web searchingPage rank ~ ~ Ch. 5 & 8 Ch. 27-28 ~ 5.3-5.4
12

[PPT]

Mar 7: Cryptography;  Security; ~ ~ ~
13

 

Mar 9: Review or Final presentations Final projects/exams due

I2N = Introduction to Networking: How the Internet Works
LWD = Learning Web Design: A Beginner’s Guide to HTML, CSS, JavaScript, and Web Graphics
P&IS = How Personal & Internet Security Work
WtIW = How the Internet Works
BoWD = Basics of Web Design
W101 = Web 101

Grading

Assignment Weight
Homework sets 50%
Project 50%

Collaboration Policy: You may freely use internet resources and your course notes in completing assignments and quizzes for this course. You may not consult any person other than the professor when completing quizzes or exams. (Clarifying questions should be directed to the professor.) On assignments you may collaborate with others in the course, so long as you personally prepare the materials submitted under your name, and they accurately reflect your understanding of the topic. Any collaborations should be indicated by a note submitted with the assignment.

Announcements

Please fill the knowledge card attached here, and send it back via email.

 

Opportunity Assessment Plan Guide

Questionaries:

A.An opportunity assessment plan is NOT a business plan. Compared to a business plan, it should:

  1. Be shorter
  2. Focus on the opportunity, not the venture
  3. Have no computer-based spreadsheet
  4. Be the basis to make the decision on whether to act on an opportunity or wait until another, better opportunity comes along

B.It should include:

1.A description of the product or service

a.What is the market need for the product or service?

b.What are the specific aspects of the product or service (include any copyright, patent or trademark information)?

c.What competitive products are available filling this need?

What are the competitive companies in this product market space? Describe their competitive behavior

e.What are the strengths and weaknesses of each of your competitors?

f.What are the unique selling propositions of this product or service?

g.What is the mission of the new venture?

h.What development work has been completed to date?

i.What patents might be available to fulfill this need?

2.An assessment of the opportunity:

a.What market need does it fill?

b.What is the size and past trends of this market?

c.What is the future growth and characteristics of this market?

d.What social condition underlines this market need?

e.What market research data can be marshaled to describe this market need?

f.What does the international market look like?

g.What does international competition look like?

h.What are total industry sales over the past five years?

i.What is anticipated growth in this industry?

j.How many new firms have entered this industry in the past three years?

k.What new products have been recently introduced in this industry?

l.What is the profile of your customers?

m.Where is the money to be made in this activity? (The activity that interests you most may be just off center from where the money to be made from this opportunity will be located.)

3.Entrepreneurial self-assessment and the entrepreneurial team:

a.Why does this opportunity excite you?

b.What are your reasons for going into business?

c.Why will this opportunity sustain you once the initial excitement subsides?

d.How does it fit into your background and experience?

e.What experience do you have and/or will you need to successfully implement the business plan?

f.Why will you be successful in this venture?

4.What needs to be done to translate this opportunity into a viable venture?

a.Examine each critical step.

b.Then think about the sequence of activity and put these critical steps into some expected sequential order.

c.How much time and how much money will each step require?

d.If you cannot self-finance, where would you get the needed capital?

 

 

MGT 436 Entrepreneurship, Istanbul Sehir University, Department of Business, Spring 2016

İstanbul Şehir University

MGT 436- Entrepreneurship

Course Title Code Semester Hour (T+P) Credit ECTS
Entrepreneurship MGT 436 Spring 2016      3    3 5
Prerequisites MGT100
Language of Instruction English
Course Type (Required/ Elective) Elective
Course Content In today’s economies, entrepreneurship is the engine of economic growth and prosperity. It is important for you to understand the underlying principles and concepts about entrepreneurship and the entrepreneurial process. This course covers the personal characteristics and qualities of the entrepreneur, innovation, creativity, opportunity assessment, and the role of entrepreneurship in developed and developing economies. Aspects of the family business, an important part of every economy, are also covered to provide an understanding of their role in a developed or emerging economy.

One of the key issues in successfully starting and growing a venture, particularly if outside capital is needed, is to create a global business plan. The development of a business plan and all of its components with particular focus on the marketing plan, financial plan, production plan, and organizational plan are presented. The various organizational structures available are discussed in terms of their applicability as well as important legal issues. The sources of capital and how to obtain them, starting, managing and growing a new venture, new venture valuation, and building a lasting venture are also discussed. Each person is to develop an opportunity assessment plan and a business plan. The business plan will be presented to venture capitalists and other investors for funding possibilities. Lectures, discussions, cases, and a few guest speakers will make this class a robust, valuable learning experience.

The primary goal of this course is to provide an understanding of entrepreneurship and the global business plan. This course will broaden a basic understanding obtained in the functional areas as they apply to new venture creation and growth, the business plan, and obtaining funding.

Learning Outcomes 1.     Integrate functional area material as it applies to starting a new venture and its growth.

2.     Develop an understanding of the role and activities of entrepreneurship in a global setting.

3.     Provide an opportunity to evaluate your own entrepreneurial tendencies and ability to create a global business plan.

4.     Understand all aspects of developing and submitting a business plan.

5.     Understand the various capital sources and the process of obtaining outside funding.

Teaching Method(s) The following teaching methods are used in this course: lectures, presentations, questions and answers, in-class discussions. Students are expected to read the assigned material before coming to the class. Students should also follow the instructor’s lectures by taking notes in class and contribute to in-class discussions. Students are waited to prepare a business plan and present it for potential investors.
Assessment Criteria Assessment Component Weight in Assessment (%)
Submissions 20
Presentations 40
Project 40
Total 100 %

 

WEEKLY PLAN

Weeks Material
1 Entrepreneurship and Entrepreneurial Mindset
2 Corporate Entrepreneurship
3 Entrepreneurial Strategy: Generating and Exploiting New Entries
4 Creativity and Business Idea
5 Identifying and Analyzing Domestic and International Opportunities
6 Protecting the Idea and Other Legal Issues
7 Apr. 6 2016, Business Plan
8 Apr. 13 2016, Marketing Plan
9 Apr, 20 2016, Organizational Plan  (Deadline for submission of project proposals)
10 Apr, 27 2016, Financial Plan
11 May, 4 2016, Sources of Capital (Deadline for submission of project reports)
12 Business Plan Presentations to Sources of Funding
13 Business Plan Presentations to Sources of Funding
14 Business Plan Presentations to Sources of Funding
TEXTBOOK
Required Textbook Hisrich, Robert D., Peters, Michael P. and Shepherd, Dean A., Entrepreneurship, 9th Edition (Chicago: McGraw-Hill/Irwin), 2013.

 

Required Submissions (Each of the Submissions has 2 weeks (14 days) deadline)

  • Mar. 23, 2016, Methods of creating new ideas. Demonstrate your understanding of methods : Brain Writing, Gordon Method, Delphi Method, Checklist Method (SCAMPER), Free Association, Forced Relationship, Collective Notebook Method, Attribute Listing, Big Dream Approach. Submission due to Mar 30, 2016.
  • Mar. 30, 2016, Identifying and Analysing Domestic and International Opportunities, Prepare an opportunity assessment plan, evaluate current micro and macro environment of your proposal, select a foreign market and prepare two opportunity assessment plans (one for Turkey and one for your selected foreign country). Answer the questionaries attached here.
  • Apr. 6, 2016, Prepare your business plan. Please remember that, there is no one size fits all formula for the business plans, but the provided check list can guide you and can be reach the checklist file from here.
  • Apr. 13, 2016, Prepare a marketing plan. Again the marketing plan can be in a very flexible format and again I am attaching a sample template with 15 Sections and you can reach the attachment from here.
  • Apr. 20, 2016, Prepare an organisational plan. Select one of three organisational structures and include all the attributes in the attached file. 
  • Apr. 27, 2016, Prepare a financial plan. You can download the template file and prepare a fictive company financial plan and the attached file can be reached from here. 

Submissions and Presentation Weeks

Student Number Team No. Submissions 1 – 10 Presentation Week (1 = May 11, 2= May 18)
210112807 1 1
212071570 8 + + 5 1 1
212111092 8 + + 5 1 1
210101291 5 + 1
210103504 1
212071179 10 5 5 + 5 1
715030036 4 3 + 3 1 1 1 1
715032192 4 5 + + 3 1 1 1
715031048 4 5 5 + 3 1 1 1 1 1
213013404 4 5 1 1 1
211641950 13 3 1 1
210111434 13 + 5 + 2 1 1 1 1
210112164 13 + + 1
210111125 13 3 5 + 1 1 1 1
212061635 9 5 + + 2 1
210121661 9 5 5 + 1
211591087 1 1
210111272 14 + + + 1
211592623 14 + + 2 1 1
210102349 14 1
212112031 14 3 5 + 2 1 1
211601595 15 + + 1 1 2
211601371 15 + + 1 2
211630986 7 5 + + 4 1 1 1 1 2
211702295 7 + + 4 1 2
212332205 7 + + 4 1 1 2
211592465 6 + 5 + 2
211550621 7 5 + + 4 1 1 1 1 1 2
211631757 6 5 + 3 2
211760225 3 5 + 4 2
211760785 3 5 + 1 1 2
212151746 3 5 5 + 4 1 1 1 2
212182215 3 5 + 4 1 2
213490265 not Submitted
314260275 1 + + 3 1 1 1 1 1 1 not Submitted
213910298 + not Submitted
212970238 5 + 5 5 1 not Submitted
213081051 2 5 + 5 1 not Submitted
211602364 11 + 5 1 not Submitted
210110515 + not Submitted
314782510 1 + + + 3 1 1 1 1 1 not Submitted
211640372 9 + 1 not Submitted
314251225 1 + + 3 1 1 1 1 1 not Submitted
213071275 2 5 5 + 5 1 1 1 1 not Submitted
213131500 not Submitted
211591168 5 3 + 5 not Submitted

Data Mining Course, Istanbul Commerce University

DERS İZLENCESİ (Syllabus)

Veri Madenciliği

Data Mining

14.00 – 17.00 Wed.

Dersi Veren : Doç. Dr. Şadi Evren ŞEKER

Instructor: Dr. Şadi Evren ŞEKER

E-Mail: datamining@sadievrenseker.com

Web Sitesi: http://sadievrenseker.com/wp/?p=558

Giriş: Günümüzde Internet’in gelişimine paralel olarak hızla gelişen trendlerden birisi de gerek internet gerekse diğer veri kaynakları üzerinde işletilen veri madenciliği çalışmalarıdır. Bu çalışmaların genel olarak amacı, verinin işlenerek faydalı sonuçların çıkarılmasıdır. Özellikle çok büyük miktarlardaki verinin işlenmesi ve bilginin değişim hızı, çeşitliliği ve güvenilirliği ile ilgili problemlerin çözülmesi için güncel ve son trend teknolojilerin katılımcılara kazandırılması, bununla birlikte klasik veri madenciliği teorisinin kazandırılarak güncel uygulamalara adapte edilmesi bu dersin hedefleri arasındadır.

Dersin Çıktıları

Bu dersin sonunda katılımcıların aşağıdaki hedeflere ulaşması beklenmektedir:

  1. Klasik veri madenciliği problemlerini anlayabiliyor olmak ve verilen bir problemin veri madenciliği dünyasındaki karşılığını anlayabiliyor olmak.
  2. Veri madenciliği dünyasındaki klasik problemleri çözebilecek yeterlilikte olmak.
  3. Veri madenciliğinde kullanılan temel yöntemleri anlayabiliyor, gerçek problemlere uygulayabiliyor ve alternatif yöntemler arasından doğru yöntemi seçebiliyor olmak.
  4. Büyük veri ve büyük veri dünyasına ait problemleri tanımlayabiliyor olmak
  5. Büyük veri dünyasındaki veri madenciliği uygulamalarını anlayabiliyor ve mevcut çözüm yöntemlerini biliyor olmak.
  6. En az bir adet veri madenciliği aracını kullanabiliyor olmak ve gerçek veriler üzerinde derste anlatılan teorik yöntemleri uygulayabiliyor olmak.
  7. Veri madenciliği çalışmalarının istatistiksel arka planını anlayabiliyor olmak ve istatistik ile veri madenciliği çalışmalarını ilintilendirebiliyor olmak.

Introduction: Data mining studies, parallel to the increasing trend of Internet technologies and all other data sources has an important impact on the computer science world. One major aim of data mining studies is processing the data and reaching knowledge level results from the data. Especially processing data in big volumes and with high speed, great variety and with trust problems are major problems of today’s data mining problems. Also the data mining theory and classical data mining approaches will be covered during the class and the practice of theoretical back ground on real world examples will be covered.

COURSE OBJECTIVES

  1. Understanding of data mining problems and ability to find a solution in data mining world for a real life problem
  2. Ability to solve generic data mining problems
  3. Understanding basic techniques in data mining world and ability to adapt these solutions into real world problems. Ability to select the correct solution methods among alternatives.
  4. Ability to define the problems of big data
  5. Understanding the data mining applications on big data problems and knowledge of current techniques
  6. Ability to use at least one data mining suite.
  7. Understanding the statistical background of data mining studies and relating the statistical methods with data mining.

Ders Kitabı (REQUIRED COURSE MATERIALS )

  1. TEXTBOOK:
    Ian H. Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques (Second Edition), Morgan Kaufmann, 2005, ISBN: 0-12-088407-0.
  2. TEXTBOOK 2:

Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeff Ullman, http://infolab.stanford.edu/~ullman/mmds/book.pdf

Derste Kullanılacak Yazılımlar (Softwares Required)

  • Weka
  • R-Project ve R Studio
  • Knime
  • Hadoop ve Mahout (belki)

Not Değerlendirmesi

  • 40% Final
  • 30% Proje
  • 30% Vize

GRADING

  • 40 % Final exam
  • 30 % Projects
  • 30% Midterm

Ders İçeriği

  1. Veri Madenciliğine Giriş (Sunumlar , PDF)

Makine Öğrenmesi, VTYS, OLAP, İstatistiksel kavramlar, KDD adımları, Uygulama problemleri. Veri kümelerinin tanınması (ilk veri kümesi olarak weather.arff)

  1. Veri Ambarları ve OLAP (Sunumlar, PDF)

Veri ambarları ve veri tabanları, çok boyutlu veri modelleri, OLAP, veri bilimi ve iş zekası kavramlarına giriş.

  1. Veri Ön işleme (Sunumlar, PDF)

Kirli ver ve veri temizleme, verinin dönüştürülmesi, boyut azaltılması, verinin ayrık hale getirilmesi, veri filtrelenmesi

  1. Sınıflandırma (Sunumlar, PDF)

Sınıflandırma problem, gözetimli ve gözetimsiz yöntemler, KNN sınıflandırma yöntemine giriş.

  1. Sınıflandırma Devam

Doğrusal ayrıma dayalı sınıflandırma yöntemleri (LDA, SVM gibi) ve çoklu sınıflarda kullanımı

  1. Bölütleme (Kümeleme)

Bölütleme problemleri, k-means algoritması,

  1. Tahmin ve Birliktelik Çıkarımı

Regrezisyon ile tahmin, Kitle kaynak kullanımı ve tahmin, apriori algoritması ile birliktelik kurallarının madenciliği

  1. Çoklu veri madenciliği yöntemlerinin kullanımı

Boosting, MaVL,

  1. Vize
  2. Yapay Sinir Ağları ve Regrezisyon Yöntemleri

Regrezisyon ve yapay sinir ağları ile klasik problemlerin çözümleri.

  1. Büyük Veriyi İşleme

Map-Reduce problemleri,

  1. Metin Madenciliği

Metin özellik çıkarım yöntemleri, Doğal dil işlemeye giriş ve problemler, uygulama olarak yazar sınıflandırma ve yazar tanıma problemi

  1. Sosyal Ağ ve Web Madenciliği

Knime ile web madenciliği uygulaması ve Twitter verisinin işlenmesi

  1. Uygulamalar

Course Outline

  1. Introduction to Data Mining (Slides in PDF)

Machine Learning, DBMS, OLAP, Statistical concepts, KDD steps, Application Problems. Introduction to data sets (weather.arff)

  1. Data Warehouse and OLAP (Slides in PDF)

Data warehouse and data bases, multi dimensional data models, OLAP, data science and business intelligence.

  1. Data Preprocessing (Slides in PDF)

Feature Extraction, Dirty data, cleaning data, transforming data, dimension reduction, discretization of data, filters, normalization

  1. Classification (Slides in PDF)

Classification problems, supervised and unsupervised learning, OneR, TwoR, KNN, Naïve Bayes

  1. Classification, Continue

Linear Classification methods (LDA, SVM gibi) multiple classification

  1. Clustering

Clustering concept, k-means,

  1. Prediction and Association Rule Mining

Regression, corwd sourcing, apriori algorithm.

  1. Multiple methods for Data Mining

Boosting, MaVL,

  1. Midterm
  2. Artificial Neural Networks, Regression Analysis

Regression methods and ANN approaches to classical data mining problems.

  1. Processing Big Data

Map-Reduce

  1. Text Mining

Feature extraction methods for texts. Introduction to natural language processing, author attribution and classification problems.

  1. Social Network and Web Mining

Web mining by Knime and a Social mining application on twitter data.

  1. Applications

Haftalık Plan (Weekly Plan)

Kitabın yazarının hazırladığı slaytlar (Slides from the Author of Book)
http://web.engr.illinois.edu/~hanj/bk3/bk3_slidesindex.htm
Bu slaytların işleniş sırası aşağıdaki şekildedir:

  • Hafta 1 : Genel giriş, dersin işlenişi, ders takvimi, ölçme ve değerlendirme kriterleri, projeler, derste anlatılacak yazılımlar ve genel olarak veri madenciliği kavramlarına giriş yapılmıştır
  • Hafta 2 (30 Eylül 2015): Chapter 1 Introduction
  • Hafta 3 (07 Ekim 2015) : Chapter 4 Data Warehousing and On-Line Analytical Processing
  • Hafta 4 (14 Ekim 2015) : Chapter 3 Preprocessing ve Weka’ya giriş (ders lab’ta yapılacak)
  • Hafta 5 (21 Ekim 2015): Chapter 8 Sınıflandırma (Classification) kavramına giriş ve bazı sınıflandırma algoritmaları
  • Hafta 6 (28 Ekim 2015): 29 Ekim Bayramı dolayısıyla ders yapılmamıştır
  • Hafta 7 ( 4 Kasım 2015): Chapter 8 Sınıflandırma (Classification) algoritmaları: KNN, OneR, ZeroR, Naive Bayes, Decision Trees, Rule Based Classification
  • Hafta 8 (11 Kasım 2015): Vize İmtihanı (sorular ve çözümleri için tıklayınız)
  • Hafta 9 (18 Kasım 2015): İleri Sınıflandırma Algoritmaları: SVM, Linear Regression, ANN, non-linear Regression

Notlar

Vize Notları için Tıklayınız.

Proje Teslim Süresi 27 Aralık 2015 Pazar akşamına kadar uzatılmıştır. ilgili tarihi taşıdığı sürece projenizi teslim edebilirsiniz (gece yarısına kadar).

Final 120 üzerindendi ancak notlar çok düşük olduğu için (orjinal notlarınzı tabloda var) final notlarını da %33 oranında yukarı çektim. Dolayısıyla vize notlarınızı 2 ile çarpıp final notlarınızı da %33 arttırmış olduk. Harf notlarınızı buna göre hesapladım ancak yine notları düşük bulduğum için bu kez birer harf ilave ederek yükselttim. Sonuçları dosyadan görebilirsiniz. Hepinize başarılı ve mutlu bir yeni yıl dilerim. Final, Proje ve Harf notlarınız için tıklayın. Lütfen itirazınız varsa en kısa sürede bana ulaşın (ben de insanın ve ne kadar dikkat edersem edeyim hata yapabiliyorum, özellikle proje konusunda ders için belirlediğimiz mail adresi dışında maillere proje gönderildiği için hepsini toparlamak çok fazla vaktimi aldı, yine de gözden kaçmış olma ihtimali var, böyle bir durum varsa veya başka bir itirazınız varsa bana en kısa sürede ulaşın).

ERP Course, Istanbul Medeniyet University

DERS İZLENCESİ (Syllabus)

Kurumsal Otomasyon Sistemleri Uygulamaları

ERP (Enterprise Resource Planning) Practices

17.30 – 20.30 (bi-weekly)

Dersi Veren : Doç. Dr. Şadi Evren ŞEKER

Instructor: Dr. Şadi Evren ŞEKER

Web Sitesi: http://sadievrenseker.com/wp/?p=559

E-Mail: erp@sadievrenseker.com

Giriş: Bu kursun amacı kurumsal otomasyon sistemlerinin teorik yapısı, bu sistemlerin gerçek dünyadaki uygulamaları, yazılım üzerinde gerçeklemeleri, bu sistemlerin alt sistemlerinin incelenmesidir. Alt sistemlerden tedarik zinciri, iş süreçleri analizleri, karar destek sistemleri, tahmin destek sistemleri, kaynak yönetim sistemleri, üretim planlama sistemleri gibi kritik sistemler özel olarak ele alınacaktır.

  • Modül 1: ERP Sistemlerine giriş, sistemlere geçiş ve uygulama
  • Modül 2: İş süreçleri ve tedarik zincirleri
  • Modül 3: ERP sistemleri ile süreç yönetimi

Introduction: Aim of this course is delivering the theoretical structure of ERP systems besides the practices in the real world. The course will give a brief information about ERP applicatinos on software development steps, as well as the sub systems of ERP systems such as, MRP, MRP 2, supply chain management (SCM), business process modeling (BPM), decision support systems (DSS), and forecasting.

  • Module 1: Overview of Enterprise Systems and Implementation/Transformation
  • Module 2: BPM & SCM
  • Module 3: Using ERP to Manage Supply Chains & Make Business Decisions

COURSE OBJECTIVES

  1. To build an understanding of the fundamental business processes used to run companies with a focus on supply chain processes, (e.g., purchase‐to‐pay, order‐to‐cash)
  2. To provide an overview of enterprise systems including their business purpose, typical modules, historical evolution, and current short‐comings and challenges experienced in industry
  3. Understand the difficulties and solutions for the implementation of ERP systems.
  4. To learn how to manage the supply chain of a company using an ERP system in simulated, real‐ time environment
  5. To learn how to effectively analyze information from an ERP system to make business decisions
  6. To gain an understanding and appreciation of the importance that leading people and managing change plays in the success of ERP implementations and long‐term competitive advantage of companies
  7. To prepare students for career opportunities in industry

Ders Kitabı (REQUIRED COURSE MATERIALS )

  1. TEXTBOOK:
    Essentials of Business Processes and Information Systems
    Simha Magal and Jeffrey Word. ISBN‐13: 978‐0‐470‐23059‐6
  2. TEXTBOOK 2:

Integrated Business Processes with ERP Systems, Simha R. Magal, Jeffrey Word, ISBN-10: 0470478446

Not Değerlendirmesi

40% Final

30% Proje

30% Case Studies

GRADING

40 % Final exam
30 % Projects

30% Case Studies

Ders Takvimi

# Tarih Konu Teorik Uygulama
1 30 Eylül Giriş Ders içeriği (bu doküman)
2 7- Ekim Kurumsal otomasyon sistemleri ve iş süreçleri Chapter 1 ‐ Magal & Word, “Hershey’s Bittersweet Lesson” –
3 14- Ekim Ders Yok Chapter 2 ‐ Magal & Word, “Slow Product Ramps‐ups Challenge Oracle and SAP”
4 21- Ekim ERP için yatırımın geri dönüşü
5 28- Ekim Ders Yok The San Diego City Schools: ERP Return on Investment case study
6 4- Kasım BPM İş Süreçleri ile ERP entegrasyonu
7 11 – Kasım Ders Yok Buradaki hikayelerden birisini seçin (değişebilir) : http://www.orbis-software.com/customers/
8 18- Kasım Kalite Kalite Yönetimi ve ERP
9 25 Kasım Ders Yok Proje konusunun seçimi ve proje teklif raporu
10 2 Aralık Üretim Süreci Tedarik Yönetim Sistemi ve ERP
11 9- Aralık Ders Yok Kalite veya tedarik zinciri ile ilgili uygulama konusu
12 16- Aralık ERP sistemlerinin uygulanması Karar destek sistemleri ve tahmin yöntemleri
13 23- Aralık Ders Yok ERPism
14 30- aralık ERP ve optimizasyon problemleri
15 13- Ocak Tahmini son tarih Final Sınavı Projelerin teslimi için son gün

 

Course Schedule

# Date Topics Theory Practice
1 30 Sept Course introduction Syllabus
2 7- Oct Overview of ERP Systems and Business Processes Chapter 1 ‐ Magal & Word, “Hershey’s Bittersweet Lesson” –
3 14- Oct No Class Chapter 2 ‐ Magal & Word, “Slow Product Ramps‐ups Challenge Oracle and SAP”
4 21- Oct Return on Investment of ERP
5 28- Oct No Class The San Diego City Schools: ERP Return on Investment case study
6 4- Nov BPM Integrating BPMs with ERP
7 11 – Nov No Class Pick one case study from : http://www.orbis-software.com/customers/
8 18- Nov The Fulfillment Process Quality and ERP
9 25 Nov No Class Pick project topics and prepare a project proposal
10 2 Dec The Production Process SCM and ERP
11 9- Dec No Class Case Study on Quality or SCM
12 16- Dec Implementation of ERP systems DSS , Forecaseting and ERP
13 23- Dec No Class ERPism
14 30- Dec Optimization and ERP
15 13- Jan Tentative Deadline Final Exam Deadline for Projects

 


Okuma Listesi (Read List)

Hafta 2: erp_hershey , erp_oracle_SAP_meydanokuma

Hafta 4: San Diego Şehir Okulları ERP Projesi ROI vaka analizi (dosyalar derste çıktı olarak verilecektir). Teslim tarihi uzatılmıştır.

 

Trajedi

 

Zor zamanlarda tatlı melodiler istersin

Günler zorken geceler dolu salonlar getirir
Ama minör akorlara geçtiğim zaman
Zor zamanlarda tatlı melodiler istersin
Günler zorken geceler dolu salonlar getirir
Ama minör akorlara geçtiğim zaman
İşte bununla izleyiciler vahşileşir

İşte trajedi burada
Acının sesi
Ağlamayı sevdiğin gözyaşları
Çare istemeyen acı
İnsanlar gülmemi isterler
Biraz Eğlence ve keyif ister
Ama bütün müzikler başladığında
Trajedinin sesini severler

İşte trajedi burada
Ölümün harmonisi
Ağlamayı sevdiğin gözyaşları
Huzurla savaşan acı
İnsanlar gülmemi isterler
Biraz Eğlence ve keyif ister
Kalbinin derinliklerinde sende trajediyi istersin

Sahneye yürürüm ve arkadaşın var şarkısını söylerim
Mutlu sonda bir tezahürat olur
Ama minör akorlara geçtiğim zaman
İşte bununla izleyiciler vahşileşir

Bir, Seni başarıya yönelteceğim
İki, her kuralı çiğneyeceğiz
Üç, Bu onların kutsadığı dramadır
Dört, içindeki hüznü bul
Beş, Bu onların taptığı göz yaşlarıdır
Altı, Ağladıklarında daha fazlasını ver

İşte trajedi burada
Ölümün harmonisi
Ağlamayı sevdiğin gözyaşları
Huzurla savaşan acı
İşte trajedi burada
Ölümün harmonisi
Ağlamayı sevdiğin gözyaşları
Huzurla savaşan acı
İnsanlar gülmemi isterler
Biraz Eğlence ve keyif ister
Kalbinin derinliklerinde sende trajediyi istersin

Istanbul Medeniyet University, Introduction to Management Course

—Course home page: http://www.sadievrenseker.com/wp/?p=458

—lecture notes, tutorials, assignment, grading, office hours, etc.

—Textbook: Management, 13/E, Stephen P. Robbins, San Diego State UniversityMary Coulter, Missouri State UniversityISBN-10: 0133910296 • ISBN-13: 9780133910292-

—Lecturer: Dr. Sadi Evren SEKER : management@sadievrenseker.com

—Grading: Class participation (5%), Project / Presentation (15%), —Midterm Exam (30%), Final exam (50%)

—Class participation includes participation in both lectures and tutorials (attendance, asking and answering questions, presenting solutions to tutorial questions).

—Note that attendance at every lecture and tutorial will be taken and constitutes part of the class participation grade.

—Midterm Exam (in class, 1 hr) and final exam (2 hrs) are both open-book

—A must check : Pearson Student Learning Center

Schedule : —”Yönetime Giriş” 13.30, Tuesday, “Introduction to Management” 14.30, Wednesday

—No Classes, Seoul between 5 – 10 March, —Dallas between 18 – 29 March, —Make up classes TBA

Course will be on time (14.30) at 25th of March. (25 Mart günü ders, saatinde (14.30) yapılacaktır).

Course Outline:

PART 1: Introduction

PART 2: Basics of Managing in Today’s Workplace

PART 3: Planning

PART 4: Organizing

PART 5: Leading

PART 6: Controlling

Slides are also available from Pearson learning center

Project deliverables will be submitted until midnight, May 1st,2015 via e-mail. Attach your presentation (preferably in power point format) and your project report (preferably in word format). Your projects should include proof of visit, biography of manager, brief explanation of subject you selected from course content and your interview questions and answers.

 

Istanbul Commerce University, Artificial Intelligence

—Course home page: http://www.sadievrenseker.com/wp/?p=449

—lecture notes, tutorials, assignment, grading, penffice hours, etc.

—Textbook: S. Russell and P. Norvig Artificial Intelligence: A Modern Approach Prentice Hall, 2009, Third Edition

—Lecturer: Dr. Sadi Evren SEKER : ai@sadievrenseker.com

—Grading: Class participation (5%), Programming assignment (15%), —Midterm Exam (30%), Final exam (50%)

—Class participation includes participation in both lectures and tutorials (attendance, asking and answering questions, presenting solutions to tutorial questions).

—Note that attendance at every lecture and tutorial will be taken and constitutes part of the class participation grade.

—Midterm Exam (in class, 1 hr) and final exam (2 hrs) are both open-book

—A must check : http://aima.cs.berkeley.edu

Schedule : —BIL 452, Monday 10.00 a.m. – 12.30 p.m. & 13.00 p.m. to 15.30 p.m.

—No Classes, Seoul between 5 – 10 March, —Dallas between 18 – 29 March, —Make up classes TBA

Midterm Grades (Vize Notları, Excel Dosyası)

Final Grades (Final Notları ve Harfler ,Excel Dosyası)

Course Outline:

  • —Introduction and Agents (chapters 1,2)
  • —Search (chapters 3,4,5,6)
  • —Logic (chapters 7,8,9)
  • —Planning (chapters 11,12)
  • —Uncertainty (chapters 13,14)
  • —Learning (chapters 18,20)
  • —Natural Language Processing (chapter 22,23)

Slides:

  • Week 1, Introduction
  • Week 2, Search
  • Week 2, 3, Heuristic Search
  • Week 3, Constraing Satisfaction Problems
  • Week 3, Satisfiability
  • Week 4, Review 1st part (Search)
  • Week 4, Review 2nd part (Heuristics)
  • Week 5, no classes
  • Week 6, Midterm : Announcement for the midterm: 1 or 2 questions with multiple parts. For example, you will be given a problem or game and you will be asked to transfer the problem into a search problem. Solve the problem with search algorithms like DFS, BFS, LDFS, IDDFS etc. and compare the success of algorithms. Also you will be asked to find two or more heuristic functions and criticize your function like if it is admissible, is any of your functions dominates another, are they suitable for greedy search or A* etc.  (this announcement is only for giving idea, midterm questions may not be limited by the definitions or examples here, so prepare for all the content we have covered until last class).
  • Exam will start at 9.00 a.m. in the morning, exam is open book and you can bring the course slides, text book or your personal notes, please be sure you have your name on all open-book material before the exam.  (Sınav sabah 9.00’da başlayacaktır, sınav açık kitap olup ders notları, kitap ve slide’ların çıktılarını getirebilirsiniz, lütfen sınavdan önce bütün yanınızda getirdiğiniz belgelerin üzerine isminizi yazınız)
  • Week 7, Midterm Review, First Order Logic,
  • Week 8, First Order Logic (Cont.), Backward and Forward Chaining
  • Week 9, Prolog Tutorial
  • Week 10, Uncertainty
  • Week 11, Knowledge Base and Rule Based Systems
  • Week 12, Data Mining Techniques, Preprocessing, Normalization (Quantile, Min-Max), Classification (K-NN), Clustering (K-Means), Prediction (Linear Regression), Association
  • Week 13, Advanced Artificial Intelligence Topics : Artificial Neural Networks (Bayesian Networks , ), Genetic Programming
  • Week 14, Project Demonstrations will start at 10 a.m. at May 11 and will finish when nobody is waiting on the class. (Proje demoları 11 Mayıs günü saat 10’da başlayacak ve sunum için gelen öğrenci kalmayana kadar devam edecektir, şayet geç gelirseniz ve kimse kalmamış olursa sunum yapma imkanınız bulunmayacağı için lütfen 10’da sunum için gelmiş olmaya dikkat ediniz).
  • Week 15, Final Exam: Exam will be open book and questions will be in similar format to the midterm exam. Exam is scheduled to 18th of May at 11.00a.m.

Sır, Mustafa Kutlu

9789759953003

Ardımdan “Efendi sırroldu” demişler.

iki nokta üst üste bütün haşmetiyle karşımda duruyor.

“Bilmek” ile “bulmak”ın aynı kökten geldiğini söyleyenler var.

Aramakla bulunmaz ama bulanlar ancak arayanlardır.

Vardı kendi bildiğince efendisinin izine düştü. Sanki suya seccade saldı.

Kalabalıkta kimsenin yüzü kendinin değildir, bilirsiniz.

Bu şehrin bir kapısından girip, öbür kapısından çıkıncaya kadar bildiklerimi unuttum, unuttuklarımı hatırladım. Var olan varlığım yok olmuş, yoktan var edilmiş idim.