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Data Analytics

Your Introduction to the World of Analytics

Learn technical concepts and get hands-on training

DAT 105 - Artificial Intelligence (AI) for Business: An Introduction C01

Academic Credit Value:
3 units
Course Delivery Mode:
Virtual Classroom
Hours of Study:
30 hours
Course Prerequisite(s):
Introductory level computer programming and statistics. This course is an inquiry/exploratory course into artificial intelligence; it is not a programming or technical course in AI.
Course Anti-requisite(s):
No Anti-requisite(s)
Instructor Name:
Cancelled
Course Dates:
09/26/2021 - 11/28/2021



Required Course Materials:
Personal Laptop Online Textbook: The Executive Guide to Artificial Intelligence, Andrew Burgess, 2018 (https://link.springer.com/book/10.1007/978-3-319-63820-1)
Optional Course Materials:
Other readings and resources will be posted in the Avenue to Learn course shell.
Course Description:

Students are advised to retain course outlines for future use in support of applications for employment or transfer of credits.

Refer to the Policy & Procedure section for further course and McMaster Continuing Education information. 

This course presents the principles of artificial intelligence (AI) through an exploration of its history, capabilities, technologies, framework, and its future. AI applications in various industries will be reviewed through some case examples. Current trends in AI will be discussed and students will be encouraged to consider the potentials of AI to solve complex problems. This course will help students to understand the implications of AI for business strategy, as well as the economic and societal issues it raises. 
Learning Outcomes:

Upon completion of this course, students will:

1. Define Artificial Intelligence (AI) and explain its history and revolution
2. Identify the impacts of AI on the society and the barriers to AI
3. Describe AI capabilities and the AI technologies
4. Discover how AI can be exploited through some case studies
5. Recognize the potential of AI in the organization
6. Identify the risks of AI
7. Explore the future of AI
8. Use some of the AI tools such as TensorFlow and the services such as IBM Watson cognitive services
9. Learn how to build an AI agent or an AI application using AI services and AI tools

Course Evaluation

Assignment/Activity

Due Date

Weightage (%)

Attendance

Every week

10

Assignment 1

Week 2

10

Assignment 2

Week 3

10

Individual/Group Project part 1

Week 5

20

Assignment 3

Week 7

10

Individual/Group Project part 2

Week 8

30

Project Presentation

Week 9

10

TOTAL

100

Course Format:
This course is designed to present the fundamental concepts and theories in data management and data analytics to promote the application to the workplace and professional practice. Course activities will include instructor presentations, required readings, and experiential learning activities (ie. case studies, group discussions, projects, etc.)
Assignment Submission:
In-class and/or A2L assignment folder(s). Consult with Instructor.
Late Coursework:
Late assignments will be subject to a 2% per day late penalty (includes weekends and holidays) for up to seven (7) days. After this date, no assignments will be accepted and a grade of zero (0) will be applied.  Extensions for course work must be approved by the instructor before the due date (see Academic Regulations below), and will be granted for illness or emergencies only. Students may be asked to submit supporting documentation for an extension request. NOTE:  This policy applies to assignments and other hand in type coursework only.  This policy does not apply to discussion board topics/postings which do not allow for late postings/contributions.

Policy & Procedures:

Academic Regulations (Attendance, Coursework, Tests/Exams):
In accordance to McMaster University’s General Academic Regulations, “it is imperative that students make every effort to meet the originally scheduled course requirements and it is a student’s responsibility to write examinations as scheduled.” Therefore, all students are expected to attend and complete the specific course requirements (i.e. attendance, assignments, and tests/exams) listed in the course outline on or by the date specified. Students who need to arrange for coursework accommodation, as a result of medical, personal or family reasons, must contact the course instructor within 48 hours of the originally scheduled due date. It is the student’s responsibility to contact the Program Manager to discuss accommodations and procedures related to deferred tests and/or examinations within 48 hours of the originally scheduled test/exam, as per policy. Failure to contact the course instructor, in the case of missed coursework, or the Program Manager, in the case of a missed test/examination, within the specified 48-hour window will result in a grade of zero (0) on the coursework/exam and no further consideration will be granted.

*Note: Supporting documentation will be required but will not ensure approval of accommodation(s).
Academic Integrity
You are expected to exhibit honesty and use ethical behaviour in all aspects of the learning process. Academic credentials you earn are rooted in principles of honesty and academic integrity. Academic dishonesty is to knowingly act or fail to act in a way that results or could result in unearned academic credit or advantage. This behaviour can result in serious consequences, e.g. the grade of zero on an assignment, loss of credit with a notation on the transcript (notation reads: “Grade of F assigned for academic dishonesty”), and/or suspension or expulsion from the university.

It is your responsibility to understand what constitutes academic dishonesty. For information on the various types of academic dishonesty please refer to the Academic Integrity Policy, located at http://www.mcmaster.ca/academicintegrity/

The following illustrates only three forms of academic dishonesty:
  1. Plagiarism, e.g. the submission of work that is not one’s own or for which other credit has been obtained.
  2. Improper collaboration in-group work.
  3. Copying or using unauthorized aids in tests and examinations.
Academic Accommodations:

ACADEMIC ACCOMMODATION OF STUDENTS WITH DISABILITIES

Students with disabilities who require academic accommodation must contact Student Accessibility Services(SAS) at 905-525-9140 ext. 28652 or sas@mcmaster.ca  to make arrangements with a Program Coordinator. For further information, consult McMaster University’s Academic Accommodation of Students with Disabilities policy.

Academic Accommodation for Religious, Indigenous or Spiritual Observances (RISO)
Students requiring academic accommodation based on religious, indigenous or spiritual observances should follow the procedures set out in the RISO policy. Students will need to contact their instructors as soon as possible to make alternative arrangements for classes, assignments, and other coursework. It is thestudent’s responsibility to contact McMaster Continuing Education to discuss accommodations related toexaminations.
On-line Elements:

Conduct Expectations:

As a McMaster student, you have the right to experience, and the responsibility to demonstrate, respectful and dignified interactions within all of our living, learning and working communities. These expectations are described in the Code of Student Rights & Responsibilities (the “Code”). All students share the responsibility of maintaining a positive environment for the academic and personal growth of all McMaster community members, whether in-person or online.

It is essential that students be mindful of their interactions online, as the Code remains in effect in virtual learning environments. The Code applies to any interactions that adversely affect, disrupt, or interfere with reasonable participation in University activities. Student disruptions or behaviours that interfere with university functions on online platforms (e.g. use of Avenue 2 Learn, WebEx or Zoom for delivery), will be taken very seriously and will be investigated. Outcomes may include restriction or removal of the involved students’ access to these platforms.

 Copyright and Recording:

Students are advised that lectures, demonstrations, performances, and any other course material provided by an instructor include copyright protected works. The Copyright Act and copyright law protect every original literary, dramatic, musical and artistic work, including lectures by University instructors.

The recording of lectures, tutorials, or other methods of instruction may occur during a course. Recording may be done by either the instructor for the purpose of authorized distribution, or by a student for the purpose of personal study. Students who wish to record sessions need to acquire permission from the instructor. Students should be aware that their voice and/or image may be recorded by others during the class. Please speak with the instructor if this is a concern for you.

Turnitin.com:
In this course, we may be using a web-based service (Turnitin.com) to reveal plagiarism. Students will be expected to submit their work electronically to Turnitin.com and in hard copy so that it can be checked for academic dishonesty. Students who do not wish to submit their work to Turnitin.com must still submit a copy to the instructor. No penalty will be assigned to a student who does not submit work to Turnitin.com. All submitted work is subject to normal verification that standards of academic integrity have been upheld (e.g., on-line search, etc.). To see the Turnitin.com Policy, please go to McMaster Academic Integrity Policy.
Course Changes:

The instructor reserves the right to modify elements of the course and will notify students accordingly.

Extreme Circumstances:

The University reserves the right to change the dates and deadlines for any or all courses in extreme circumstances (e.g., severe weather, labour disruptions, etc.). Changes will be communicated through regular McMaster communication channels, such as McMaster Daily News, A2L and/or McMaster email.

Course Withdrawal Policy:
Policies related to dropping a course and course withdrawals are posted to the Centre for Continuing Education’s program webpage, FAQs & Policies (https://www.mcmastercce.ca/cce-policies#Dropping).
Storm Closure Policy:

In the event of inclement weather, the Centre for Continuing Education will abide by the University’s Storm Closure Policy: https://www.mcmaster.ca/policy/Employee/storm_emergency_policy.pdf, and will only close if the University is closed. All in-class courses, exams and room bookings by internal and external clients will be cancelled if the Centre for Continuing Education is closed. On-line courses will take place as scheduled.

Grading Scale:
 Grade Equivalent
Grade Point
Equivalent Percentages
A+ 12 90-100
A 11 85-89
A- 10 80-84
B+ 9 77-79
B 8 73-76
B- 7 70-72
C+ 6 67-69
C 5 63-66
C- 4 60-62
D+ 3 57-59
D 2 53-56
D- 1 50-52
F 0 0-49
Course Schedule:

Module #

Date (Week)

Module Name

Required Readings

Activities Due

1

Week 1

· Definition of AI

· The History of AI

· The AI Revolution

· Introduction to IBM Watson

Textbook: Chap 1 and Chap 2

First chapter of Wolfgang Ertel book

IBM Watson

 

2

Week 2

· AI and society

· Agents and Knowledge-based systems

· Barriers to AI

· IBM Watson Assistant

Textbook: Chap 1 and Chap 2

First chapter of Wolfgang Ertel book

IBM Watson Assistant

Assignment 1

3

Week 3

· AI Capabilities

· AI Framework

· IBM Watson speech to text and text to speech services

Textbook: Chap 3

IBM Watson S2T and T2S

Assignment 2

4

Week 4

· AI and associated technologies

· IBM Watson Personality Insights and Tone Analyzer

Textbook: Chap 4

IBM Watson PI and TA

 

5

Week 5

· AI in action (case studies)

· IBM Watson Discovery

Textbook: Chap 5

IBM Watson Discovery

Individual/Group Project part 1

6

Week 6

· How to adopt AI in your Business?

· IBM Watson Natural Language Understanding

Textbook: Chap 6

IBM Watson NLU

 

7

Week 7

· How to implement AI in your business

· AI Risks

· IBM Watson Explorer

Textbook: Chap 7 and Chap 8

IBM Watson Explorer

Assignment 3

8

Week 8

· Industrializing AI

· Future of AI

· Introduction to TensorFlow

Textbook: Chap 9 and Chap 10

TensorFlow

Individual/Group Project part 2

9

Week 9

· Course Summary

· Project Presentation/Demo