Proposed Syllabus for ISM 360: Artificial Intelligence for Business

Instructor: Dr. Liping Liu, 360 CBA Building, +5947, liping@uakron.edu

Credits: 3 hours

Text Books:

• Miroslav Kubat, Fundamentals of Artificial Intelligence: Problem Solving and Automated Reasoning, McGraw Hill, 2023. 
• Liping Liu, Lecture Notes on Artificial Intelligence

Time and Location: Mondays and Wednesdays: 3:30-4:45 PM; August 26-December 10, 2025. Regular Classroom: CBA 176 (Computer Lab).

Office Hours: 1:30-3:30 PM Mondays and Wednesdays (No appointments are necessary).

Course Description: Artificial Intelligence (AI) is a study on how to create programs (agents or robots) that receive percepts and take actions without programming. AI enables businesses to be competitive today and will become a necessity for them to survive in the future. This course  introduces the fundamental concepts and problem-solving techniques of AI in business contexts and studies how AI algorithms and models may be used to transform business processes. It covers data-light version of problem-solving approaches such as search, optimization, and constraint satisfaction and emphasizes big data version of automated decision-making, inference-making, and knowledge acquisition, representation, and integration via propositional logic, Bayesian networks, neural networks, fuzzy logic, and evidential theory. This course will use Python programming for exercises. Prerequisite: ISM 250 and MGMT 305

Philosophy: This course is designed with the following considerations:

• Deep learning is currently the most popular approach to AI, but there are other promising alternatives such as Bayesian learning and genetic programming. This course puts all these approaches in a broad context of inquires into creating automatic programs without human coding. 
• While similar courses in Computer Science emphasize the mathematical foundation and algorithmic efficiencies of AI models and algorithms, this course focus on apply the models and algorithms to solve business problems.
• This course will reinforce student's Python programming skills and enhance their mathematical readiness in matrix, probability theory, and optimization theory for the study of AI. 
• This course prepares the foundation for modern deep learning and generative AI in ISM 422 (see https://www.ecourse.org/news.asp?which=6662).

Course Objectives: Upon satisfactory completion of this course, a student should be able to

1. Understand the fundamental concepts and techniques of AI and its applications to business operations, decision-making, and process re-engineering
2. Understand the common methods for knowledge acquisition, representation, and reasoning in AI for business applications
3. Use the language of matrices and tensors and probabilistic reasoning for understanding AI literature and predictive analytics for business operations
4. Use Python language to code common AI algorithms in search, optimization, constraint satisfaction as well as representing, learning, integrating business knowledge using Bayesian networks.  

Weekly Schedule:

• Week 1: Introduction to AI: Turing tests, algorithms, different approaches to AI, agent architecture, reflex and learning agents, AI use cases for business: apply AI for daily business operations, reengineer business process using AI, and integrate AI to improve legacy information systems. 
• Week 2: Mathematics for AI: matrix and tensor operations and Numpy and PyTorch packages for array operations, eigen value, determinant, positive definite. Examples: google page rank, recommender system, Markov chain for ice cream demand, images and videos as tensors
• Week 3: Mathematics for AI: probability distributions for business predications, generative AI and model selection, Bayesian reasoning, likelihood, Naive Bayesian. Examples: Naive Bayesian classifiers of customer reviews, simulation for bank capacity planning, comparison of machine reliabilities using F-test.  
• Week 4: Mathematics for AI: optimization calculus, gradients, convex and concave objective functions, Hessian matrix, multicriteria decision-making, decision-making under uncertainty. Examples: parametric estimation for linear regression using least square method and logistic classifiers using maximum likelihood. 
• Week 5: Problem solving via blind search: breadth first and depth first. Examples: the water-jug problem, the wolf-sheep-vegetable problem. Python coding:  implement breadth first and depth first searching algorithm to start from a node in a network and search all the connected nodes and construct a search tree. Assignment: 1) Use ChapGPT to generate Python code using breadth first search for the water-jug problem and run the code to generate a search tree; 2)Use ChapGPT to generate Python code using depth first search for the wolf-sheep-vegetable problem and run the code to generate a search tree.
• Week 6: Problem solving via heuristic search: hill-climbing and best-first search, evaluation functions, simulated annealing, gradient decent. Examples: Python coding for magic square and stochastic gradient decent. 
• Week 7: Automated planning for designing assembly lines: describing states and actions, constraints, STRIPS. Examples: Python coding for job-shop scheduling and package delivery. 
• Week 8: Exam I
• Week 9: Business knowledge representation via predicate logics: create knowledge base using Prolog and query the knowledge base.  Propositional logics, conjunction, disjunction, implication, truth table, inferences, theorem proof, conjunctive normal form, forward and backward chaining, backtracking algorithm and local search for model checking. 
• Week 10: Learning business knowledge as rules: decision trees, C5.0 algorithm, entropy, information gain, test of conditional independence, Occam's razor, tree pruning. Example: identify risky bank loans. 
• Week 11: Business knowledge representation via Bayesian networks: conditional independence and d-separation, moral graph, Markov property, conditional probabilities, prior and posterior knowledge, marginal and joint probabilities. Example: model Chest Clinic problem as Baye net, using hidden Markov fields to predict product demand. 
• Week 12: Business decision making using Bayesian networks: cliques, Markov trees, Markov blanket, local propagation architectures, soft and hard evidence. Example: product success prediction using Baye net
• Week 13: Learning business knowledge as Bayes networks: information criteria, score-based and constraint-based structural learning, fitting Bayes networks to data, Bayesian modeling and learning using "bnlearn" package. Example: learning Bayes net using ALARM data. 
• Week 14: Business knowledge representation and integration using Dempster-Shafer Theory: ignorance, mass functions, belief and plausibility functions, Dempster's rule of combination for knowledge integration and information fusion. 
• Week 15: Final Exam (Dec 19-13, 2025)

Exams: This course will have two major exams as scheduled above. Each exam includes both multiple choice and hands-on questions.

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 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.