Instructor: Dr. Liping Liu, College of Business Building 360, 330-972-5947

Office Hours: 3:00-5:00 PM Mondays and Wednesdays

Text Books and References:

• A compilation of lecture notes, book chapters, articles, and videos on Artificial Intelligence, Deep Learning, Python, and AI tools

Course Description: This is an introductory course to Artificial Intelligence and its applications to business. It is designed to serve two purposes. First, it teaches students to be literate on artificial intelligence and its tools and techniques to become managerial end users of artificial intelligence. Second, it introduces the fundamental concepts and technics and teaches students to become decision makers in the management of artificial intelligence integration and applications.

Prerequisites: The prerequisite for this class is 6200:250: Computer Applications. In particular, students are expected to be proficient in using PC and some basic still of using Excel.

Daily Schedule:

• Week 1: Introduction (Brief history and topic areas of AI, success stories, and course setup, installation of Anaconda, a tour of Jupyter Notebook, and ChatGPT license retrieval) Assignments: installing Anaconda and Python on personal computer and document the steps of installation as a report (check for completion)
• Week 2: Prompt Engineering (in-context learning and prompt techniques: zero-shot, few-shot, chain-of-thought prompting). Exercise 1: use CoT technique to prompt ChatGPT for solving homework questions, translate languages, and write a literature review on a topic. Exercise 2: use DALL-E to generate arts and product designs with specific art styles and modifiers in image quality, lighting, situations, etc. Assignment: Use ChatGPT to create your own resume to your satisfaction and submit a list of your prompts and responses as a Word document (check for completion).
• Week 3: Python Coding Basics (using Jupyter Notebook as a calculator to get familiar with python data structures (strings, numbers, and date/times, lists), for-loop control to process lists, and if-else decision control to process list items selectively.) Assignment: Create lists of student ages and another list of their part-time work earnings, and compute the total amount of earnings and the average amount of earnings by students who are age 20 or over. (check for correctness)
• Week 4: Prompt Engineering in Jupyter Notebook (Use Python code in Jupyter Notebook to query ChatGPT: Exercise 1: send a list of customer reviews on a product,  ask ChatGPT to create a summary of the reviews into an HTML table, JSON data objects, and an Excel spreadsheet on the issues mentioned in the reviews, and write a letter to the Production Department and another letter to Sales Department for actions addressing the issues. Exercise 2: send a financial document and ask ChatGPT to extract financial information and output the result as HTML table, JSON data objects, and an Excel spreadshsheet.
• Week 5: Exam I
• Week 6: Prompt Engineering for Data Management (database, primary keys, and foreign keys, SQL queries, specify table names and the columns of each table for human resource management and ask ChatGPT to generate SQL commands to create tables, insert sample data, and retrieve information in Microsoft Access, mySQL, and Oracle. Assignment:  specify table structures of a sales databases including customers, orders, orderlines, and products. Then ask ChatGPT to create SQL commands for MS Access to create the tables, insert sample data, and retrieve the total amount of orders made by each customer. (Check for completion)
• Week 7: Python Coding Basics (turn python lists into numpy arrays and pandas time series and data frames, load Excel data into data frames) Assignment: create five lists of student names, birth dates, majors, admission dates, part-time work earnings. Then create a data frame using the list, save the data frame as an Excel file, and load the file back to Jupyter Notebook. (check for completion)
• Week 8: Python and Prompt Engineering for Excel Modeling (Exercise 1: load data from Excel into Jupyter notebook and summarize and visualize data using Python code. Exercise 2: Use ChatGPT to generate python code and modify and run code for creating an Excel spreadsheets for products including their description, sizes, colors, in-stock quantities, and prices, generate code to compute total inventory values, compute the number of products which is under-stocked. Assignment: Use ChatGPT to generate code to create an Excel spreadsheet to manage personal daily expenses, compute total expenses every month, and plot the trend of monthly spendings. (check for completion)
• Week 9: Prompt Engineering for Information Security (cryptography, public and private keys, and hash functions, generate python code to encrypt and decrypt accounting data)
• Week 10: Exam II
• Week 11: Artificial Intelligence: introduction to neural networks and deep learning (artificial neurons, layers, feed forward, backpropagation, activation functions). Assignment: Create a table to summarize the following activation functions, including their function forms, common use cases, and their pros and cons)
• Week 12: Artificial Intelligence: create neural networks with TensorFlow for linear regression (keras and tensor flows tools, dense layers, input shapes, loss, metrics, optimizers, training and evaluation) Assignment: predict insurance claim costs using a network of four layers of 64 neurons with relu activation, 32 neuros with relu activation, dropout with rate = 0.5, and output layer of one neuron with linear activation. 
• Week 13: Recurrent Neural Networks for Time Series (dependency of training examples, input tensors, output tensors, input shape for time series, predict stock opening prices based on prior 30 days stock's opening, closing, and volume data). Homework: predict the world population using the last ten year's trend
• Week 14: Recurrent Neural Networks for Language Modeling (word embedding, how does computer understand the meaning of a word, how to feed sentences and articles into neural networks, sentiment analysis on product reviews and financial statements). Exercise: create and build a neural network for classification of product reviews.
• Week 15: Final Exam

Objectives: Through this course, the student should be able to

• Understand neural network models and key concepts behind popular AI tools
• Learn how to create prompts for popular AI tools and use the tools to generate audio, video, art, texts, and code for data management, spreadsheet modeling, information security, and data analytics
• Understand basic Python programming concepts and apply them to modify and run Python code

Exams: This course will have three major exams as scheduled above. Each exam includes both hands-on and written problems.

Assignments: Homework is assigned once a week for 12 weeks; each consists of conceptual questions and hands-on projects classified into three grading categories: correctness, closeness, and completeness. The correctness problems will be graded by ecourse.org, and closeness questions are graded and/or commented by instructors. Students will earn points automatically for each completeness question if it is finished (it has to be deemed complete). Assignments are due at the beginning of classes meetings on Mondays (except for holidays). No late homework will be graded. Please show your work in a neat and orderly fashion. Write or type your work on one side and in every other line. Use standard size paper (8 1/2'' by 11''). Do not use spiral notebook paper.

Attendance: Attendance is MUST and will be 10% of your final grade. Attendance will be managed by ecourse.org. The formula for computing your attendance grade is non-linear. It will take one point off for the first absence, 2 points off the second, 3 points off the third, and 4 points off the fourth. If you missed the equivalent of three-week classes, you fail the course automatically. Under special situations, you can take some classes online with the following guidelines:

1. You must obtain permission from the instructor at least one day ahead of each online session
2. Follow the lectures or recordings to perform all in-class hands-on exercises and take notes. Within one day from the class submit your notes and the finished exercises to ecourse.org as Proof of Attendance.
3. All weekly assignments are due at the same time as in-person classes. All exams must be onsite.

Quizzes: I will use quizzes regularly to check your completion or preparation of assignments

Makeup: Each student with appropriate excuses may have at most one chance to makeup homework or quiz. Note that it is your privilege but not your right to have this special favor. Also, all makeups must be completed within one week of due date and before answer key is released. 

Grades: Your final grades will be calculated by the following formulas:

35% (HW) + 55% (Tests) + 10% (Attendance)

A = 93-100%; A– = 90-92%; B+ = 87-89%; B = 83-86%; B– = 80-82%; C+ = 77-79%; C = 73-76%; C– =70-72%; D = 60-69%; F = 59% and less

Misconduct: Academic misconduct by a student shall include, but not limited to: disruption of classes, giving and receiving unauthorized aid on exams or in the preparation of assignments, unauthorized removal of materials from the library, or knowingly misrepresenting the source of any academic work. Academic misconduct by an instructor shall include, but not limited to: grading student work by criteria other than academic performance or repeated and willful neglect in the discharge of duly assigned academic duties. Convicted violations may result in grade penalties, besides the school official ones, such as increased scrutiny of future submissions, reduced benefits of curving, if any, and/or the reduction of overall grade. 

On Collaboration: All for-credit assignments, except for those designated as group projects, must be done independently, and collaboration in providing or asking for answers to those assignments constitutes cheating. 

On AI Tools: In this class, I allow students to use AI tools to help their learning. However, submitting AI generated work in lieu of your own personal contribution for credits is a violation of academic code. If a submitted work is suspected to be AI generated, the student will be asked to reproduce the submitted work in front of the instructor. 

Looking  for additional help? Students looking for additional assistance outside of the classroom are advised to consider working with a peer tutor through Knack. The University of Akron CBA has partnered with Knack to provide students with access to verified peer tutors who have previously aced this course. To view available tutors, visit uakron.joinknack.com and sign in with your student account. At the same time, if you are doing well in this class, please go to uakron.joinknack.com where you can create a verified tutoring profile and begin helping other students.