Texas McCombs School of Business
Post Graduate Program in Artificial Intelligence and Machine Learning: Business Applications
Executive-focused postgraduate program designed to equip business and technology leaders with the knowledge and practical skills to identify, evaluate, and implement AI and machine learning solutions that drive innovation, operational efficiency, and competitive advantage. Emphasis on AI strategy, Generative AI, machine learning applications, governance, and enterprise transformation.
Key Skills:
Artificial Intelligence (AI) • Machine Learning • Generative AI • Predictive Analytics • Business Intelligence • Data-Driven Decision Making • AI Strategy • AI Governance • Large Language Models (LLMs) • Retrieval-Augmented Generation (RAG) • Digital Transformation • Data Visualization • Python • Business Applications of AI
Course Projects
Natural Language Processing with Gen AI
Project 5: Medical Assistant
Business Context
The healthcare industry is rapidly evolving, and professionals face increasing challenges in managing vast volumes of medical data while delivering accurate and timely diagnoses. Quick access to comprehensive, reliable, and up-to-date medical knowledge is critical for improving patient outcomes and ensuring informed decision-making in a fast-paced environment.
Healthcare professionals often encounter information overload, struggling to sift through extensive research and data to create accurate diagnoses and treatment plans. This challenge is amplified by the need for efficiency, particularly in emergencies, where time-sensitive decisions are vital. Furthermore, access to trusted, current medical information from renowned manuals and research papers is essential for maintaining high standards of care.
To address these challenges, healthcare centers can focus on integrating systems that streamline access to medical knowledge, provide tools to support quick decision-making and enhance efficiency. Leveraging centralized knowledge platforms and ensuring healthcare providers have continuous access to reliable resources can significantly improve patient care and operational effectiveness.
Objective
As an AI specialist, your task is to develop a RAG-based AI solution using renowned medical manuals to address healthcare challenges. The objective is to understand information overload, apply AI techniques to streamline decision-making, analyze its impact on diagnostics and patient outcomes, evaluate its potential to standardize care practices, and create a functional prototype demonstrating its feasibility and effectiveness.
Questions to Answer
- What is the protocol for managing sepsis in a critical care unit?
- What are the common symptoms of appendicitis, and can it be cured via medicine? If not, what surgical procedure should be followed to treat it?
- What are the effective treatments or solutions for addressing sudden patchy hair loss, commonly seen as localized bald spots on the scalp, and what could be the possible causes behind it?
- What treatments are recommended for a person who has sustained a physical injury to brain tissue, resulting in temporary or permanent impairment of brain function?
- What are the necessary precautions and treatment steps for a person who has fractured their leg during a hiking trip, and what should be considered for their care and recovery?
Data Dictionary
The Merck Manuals are medical references published by the American pharmaceutical company Merck & Co., that cover a wide range of medical topics, including disorders, tests, diagnoses, and drugs. The manuals have been published since 1899 when Merck & Co. was still a subsidiary of the German company Merck.
The manual is a PDF with over 4,000 pages divided into 23 sections.
Introduction to Neural Networks
Project 4 - ReneWind
Business Context
Renewable energy sources play an increasingly important role in the global energy mix, as the effort to reduce the environmental impact of energy production increases.
Out of all the renewable energy alternatives, wind energy is one of the most developed technologies worldwide. The U.S. Department of Energy has put together a guide to achieving operational efficiency using predictive maintenance practices.
Predictive maintenance uses sensor information and analysis methods to measure and predict degradation and future component capability. The idea behind predictive maintenance is that failure patterns are predictable and if component failure can be predicted accurately and the component is replaced before it fails, the costs of operation and maintenance will be much lower.
The sensors fitted across different machines involved in the process of energy generation collect data related to various environmental factors (temperature, humidity, wind speed, etc.) and additional features related to various parts of the wind turbine (gearbox, tower, blades, break, etc.).
Objective
“ReneWind” is a company working on improving the machinery/processes involved in the production of wind energy using machine learning and has collected data on generator failure of wind turbines using sensors. They have shared a ciphered version of the data, as the data collected through sensors is confidential (the type of data collected varies with companies). Data has 40 predictors, 20000 observations in the training set, and 5000 in the test set.
The objective is to build various classification models, tune them, and find the best one that will help identify failures so that the generators can be repaired before failing/breaking to reduce the overall maintenance cost.
The nature of predictions made by the classification model will translate as follows:
- True positives (TP) are failures correctly predicted by the model. These will result in repair costs.
- False negatives (FN) are real failures where there is no detection by the model. These will result in replacement costs.
- False positives (FP) are detections where there is no failure. These will result in inspection costs.
It is given that the cost of repairing a generator is much less than the cost of replacing it, and the cost of inspection is less than the cost of repair.
“1” in the target variable should be considered as “failure” and “0” represents “No failure”.
Data Dictionary
The data provided is a transformed version of the original data which was collected using sensors.
- Train.csv - To be used for training and tuning of models.
- Test.csv - To be used only for testing the performance of the final best model.
Both datasets consist of 40 predictor variables and 1 target variable.
Advanced Machine Learning
Project 3 - Easy Visa
Business Context
Business communities in the United States are facing high demand for human resources, but one of the constant challenges is identifying and attracting the right talent, which is perhaps the most important element in remaining competitive. Companies in the United States look for hard-working, talented, and qualified individuals both locally as well as abroad.
The Immigration and Nationality Act (INA) of the US permits foreign workers to come to the United States to work on either a temporary or permanent basis. The act also protects US workers against adverse impacts on their wages or working conditions by ensuring US employers' compliance with statutory requirements when they hire foreign workers to fill workforce shortages. The immigration programs are administered by the Office of Foreign Labor Certification (OFLC).
OFLC processes job certification applications for employers seeking to bring foreign workers into the United States and grants certifications in those cases where employers can demonstrate that there are not sufficient US workers available to perform the work at wages that meet or exceed the wage paid for the occupation in the area of intended employment.
Objective
In FY 2016, the OFLC processed 775,979 employer applications for 1,699,957 positions for temporary and permanent labor certifications. This was a nine percent increase in the overall number of processed applications from the previous year. The process of reviewing every case is becoming a tedious task as the number of applicants is increasing every year.
The increasing number of applicants every year calls for a Machine Learning based solution that can help in shortlisting the candidates having higher chances of VISA approval. OFLC has hired the firm EasyVisa for data-driven solutions. You as a data scientist at EasyVisa have to analyze the data provided and, with the help of a classification model:
- Facilitate the process of visa approvals.
- Recommend a suitable profile for the applicants for whom the visa should be certified or denied based on the drivers that significantly influence the case status.
Data Description
The data contains the different attributes of the employee and the employer. The detailed data dictionary is given below.
- case_id: ID of each visa application
- continent: Information of continent of the employee
- education_of_employee: Information of education of the employee
- has_job_experience: Does the employee has any job experience? Y= Yes; N = No
- requires_job_training: Does the employee require any job training? Y = Yes; N = No
- no_of_employees: Number of employees in the employer's company
- yr_of_estab: Year in which the employer's company was established
- region_of_employment: Information of foreign worker's intended region of employment in the US.
- prevailing_wage: Average wage paid to similarly employed workers in a specific occupation in the area of intended employment. The purpose of the prevailing wage is to ensure that the foreign worker is not underpaid compared to other workers offering the same or similar service in the same area of employment.
- unit_of_wage: Unit of prevailing wage. Values include Hourly, Weekly, Monthly, and Yearly.
- full_time_position: Is the position of work full-time? Y = Full-Time Position; N = Part-Time Position
- case_status: Flag indicating if the Visa was certified or denied
Machine Learning
Project 2 - Personal Loan Campaign
Business Context
AllLife Bank is a mid-sized, fast-growing US-based financial institution that offers a range of retail banking services, including savings and checking accounts, fixed deposits, and personal loans. The bank’s business model is centered on building long-term customer relationships, expanding its retail footprint, and growing its loan portfolio to drive sustainable profitability through interest income.
It currently relies on a large base of liability customers (depositors) but faces a significant under-representation of asset customers (borrowers). To drive profitability through interest income, the bank must aggressively expand its loan portfolio by converting existing depositors into personal loan customers.
Last year’s pilot campaign achieved a 9% conversion rate, validating the potential of this strategy. However, to optimize marketing spend and improve efficiency, the retail marketing department requires a more data-driven approach. Enhancing the success ratio of these campaigns is critical for sustainable growth and maximizing customer lifetime value.
Objective
The objective is to develop a predictive classification model that identifies patterns and key factors driving personal loan adoption among existing liability customers. By uncovering the demographic and behavioral drivers of loan conversion, the goal is to enable targeted segmentation and more precise marketing interventions that improve campaign conversion rates, optimize marketing spend, and enhance overall profitability through higher-quality loan portfolio growth.
Data Dictionary
- ID: Customer ID
- Age: Customer’s age in completed years
- Experience: # years of professional experience
- Income: Annual income of the customer (in thousand dollars)
- ZIP Code: Home Address ZIP code.
- Family: The family size of the customer
- CCAvg: Average spending on credit cards per month (in thousand dollars)
- Education: Education Level. 1: Undergrad; 2: Graduate;3: Advanced/Professional
- Mortgage: Value of house mortgage if any. (in thousand dollars)
- Personal_Loan: Did this customer accept the personal loan offered in the last campaign?
- Securities_Account: Does the customer have a securities account with the bank?
- CD_Account: Does the customer have a certificate of deposit (CD) account with the bank?
- Online: Do customers use Internet banking facilities?
- CreditCard: Does the customer use a credit card issued by any other Bank (excluding All Life Bank)?
Python Programming
Project 1 - FoodHub
Business Context
The number of restaurants in New York is increasing day by day. Lots of students and busy professionals rely on those restaurants due to their hectic lifestyles. Online food delivery service is a great option for them. It provides them with good food from their favorite restaurants. A food aggregator company FoodHub offers access to multiple restaurants through a single smartphone app.
The app allows restaurants to receive a direct online order from a customer. The app assigns a delivery person from the company to pick up the order after it is confirmed by the restaurant. The delivery person then uses the map to reach the restaurant and waits for the food package. Once the food package is handed over to the delivery person, he/she confirms the pick-up in the app and travels to the customer's location to deliver the food. The delivery person confirms the drop-off in the app after delivering the food package to the customer. The customer can rate the order in the app. The food aggregator earns money by collecting a fixed margin on the delivery order from the restaurants.
Objective
The food aggregator company has stored the data of the different orders made by the registered customers in their online portal. They want to analyze the data to get a fair idea about the demand of different restaurants which will help them in enhancing their customer experience. Suppose you are hired as a Data Scientist in this company and the Data Science team has shared some of the key questions that need to be answered. Perform the data analysis to find answers to these questions that will help the company improve its business.
Data Description
The data includes various information related to a food order. A detailed data dictionary is provided below.
Data Dictionary
- order_id: Unique ID of the order
- customer_id: ID of the customer who ordered the food
- restaurant_name: Name of the restaurant
- cuisine_type: Cuisine ordered by the customer
- cost_of_the_order: Cost of the order
- day_of_the_week: Indicates whether the order is placed on a weekday or weekend (The weekday is from Monday to Friday and the weekend is Saturday and Sunday)
- rating: Rating given by the customer out of 5
- food_preparation_time: Time (in minutes) taken by the restaurant to prepare the food. This is calculated by taking the difference between the timestamps of the restaurant's order confirmation and the delivery person's pick-up confirmation.
- delivery_time: Time (in minutes) taken by the delivery person to deliver the food package. This is calculated by taking the difference between the timestamps of the delivery person's pick-up confirmation and drop-off information