Proposed Syllabus for ISM 420: Artificial Intelligence for Business

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

Credits: 3 hours

Text Books:

• Stuart Russel and Peter Norvig, Artificial Intelligence - A Modern Approach, Pearson; 4th edition (May 13, 2021)
• 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 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. It covers data-light version of decision making approaches such as search, optimization, and constraint satisfaction and focuses on 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 310

Philosophy: 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 into a broad context of inquires into creating automatic programs without human coding. Also, this course will reinforce student's Python programming skills and enhance their mathematical readiness in matrix and probability for AI. 

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

1. Understand the broad context of AI in history, evolution, society, business, and academic inquiries. 
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 logic 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, five tribes of AI, agents, agent architecture, reflex and learning agents, 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 package for array operations. 
• Week 3: Mathematics for AI: probability distributions for business predictions and generative AI, Bayesian reasoning, sentiment analysis with Naive Bayesian. Python scikit-learn package for Naive Bayesian classifiers of customer reviews
• Week 4: Mathematics for AI: gradients, Hessian matrix, multicriteria decision-making, decision-making under uncertainty
• Week 5: Business decision-making as search problems, best-first, breadth-first, depth-first, heuristic search, hill-climbing, simulated-annealing, evolutionary algorithms, Python recursion
• Week 6: Business decision making as constraint satisfaction problems, constraint propagation, Python coding for job-shop scheduling.
• Week 7: Exam I
• Week 8: Representing business knowledge using propositional logics: atomic sentences, conjunction, disjunction, implication, truth table, inferences, theorem proof, conjunctive normal form, forward and backward chaining, backtracking algorithm and local search for model checking
• Week 9: Machine Learning of Business Propositions: decision trees, C5.0 algorithm, entropy, information gain, test of conditional independence, tree pruning
• Week 10: Representing business knowledge under uncertainty using Bayesian networks: conditional independence, d-separation, moral graph, using hidden Markov fields to predict product demand. 
• Week 11: Making inferences and decisions with Bayesian networks: cliques, Markov trees, Markov blanket, local propagation architectures, soft and hard evidence, product success prediction using Baye net
• Week 12: Machine Learning of Bayes networks for business decision-making: maximum likelihood, information criteria, Occam's razor, score-based and constraint-based structural learning, Bayesian modeling and learning using "bnlearn" package
• Week 13: Representing business knowledge using neural networks: artificial neurons, activation functions, neural network representation of proposition logics,  python coding of feedforward propagation
• Week 14: Machine learning of neural networks for business predictions and classifications: python coding for back propagation, mathematics for batch gradient decent for minimizing SSE and Cross-Entropy using ReLU, sigmoid, step, and linear activation, using tensorflow package for linear and logistic regressions. 
• 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.