MODULE 1: THE BUSINESS OF AI
Delve into AI project development, assess business impact, and explore case studies illuminating AI applications and limitations. Create effective POC (Proof of Concept) plans, understand market dynamics, and utilize data for decision-making steering the business into the future of AI.
Explore practical applications and limitations of AI in business
Engage in the step-by-step process of developing an AI project
Evaluate ROI, threats & opportunities
Develop a POC outline, evaluate solutions, and assess market potential
Create a roadmap for product development and expansion
MODULE 2: DATA MODELING
Develop skills in uncovering patterns and making informed, data-driven decisions. Explore Regression, including multivariate Linear Regression, and delve into classification using Logistic Regression. Gain practical insights for personalized solutions and unlock new possibilities within your data.
Understand data objects and visual metrics for data interpretation
Explore Data Visualization and manipulation
Learn Machine Learning fundamentals and Regression techniques
Delve into Logistic Regression and evaluate classification models
Assess different performance measures—precision and recall
Engage in case study sessions with experts to apply concepts
MODULE 3: NEURAL NETWORKS & ENSEMBLE TECHNIQUES
Understand how neural networks mimic human thinking for better decision-making. Learn to build, train, and optimize neural networks, correcting prediction errors. Explore Decision Trees, including CART and pruning techniques, and advance to Random Forests for more accurate guesses.
Gain foundational knowledge on Neural Networks
Explore techniques for optimizing Neural Networks
Dive into Decision Trees and learn about Pruning techniques
Delve into Logistic Regression and evaluate classification models
Understand ensemble techniques as a powerful tool in Machine Learning
Solidify understanding of these concepts using case studies
MODULE 4: DISCOVERING PATTERNS IN DATA
Explore clustering to unveil hidden data patterns, mastering the grouping, scaling, and visualization of similar data points for strategic decisions. Dive into recommendation systems using content-based and collaborative filtering for anticipating customer preferences. Learn to leverage data for business growth and build effective systems.
Gain expertise in K-Means clustering, scaling, and visual analysis
Learn content-based and collaborative filtering
Understand similarity measures, and explore hybrid systems
Understand the real-world applications of clustering and reinforce knowledge
Understand ensemble techniques as a powerful tool in Machine Learning
Develop practical skills in applying clustering and recommendation systems
MODULE 5: DEEP LEARNING FOR LEADERS
Unlock insights with NLP tasks like sentiment analysis and machine translation. Develop practical models for data-driven decisions. Explore Computer Vision, decoding images and videos with transformative applications in self-driving cars and virtual reality, enhancing business with visual data.
Understand NLP fundamentals and methodologies for problem-solving
Learn techniques for text extraction and web scraping in NLP
Engage in the practical process of building NLP models
Explore basics of Computer Vision (CV) and its problem types
Delve into the workings of Convolutional Neural Networks in CV
Apply concepts through case studies and gain insights from experts
SELF-PACED MODULES
Concepts: Role of data, Statistical Techniques, Paradigms in Data Science, Data-Driven Solutions, Lifecycle of Solutions.
Concepts: Introduction to Generative AI, Introduction to ChatGPT, Generative AI - Demos
Concepts: Service Vs Product Companies, AI Team Composition, Centralized Vs Distributed AI Teams, Handling Resistance, Handling Resistance From Management, Managing Portfolio of Projects, Scaling AI Teams
Concepts: Applications of Transfer Learning, Dealing with Imbalanced Data: Data Augmentation, Model Deployment, Modes of Training, Serialization, Model Monitoring and Recalibration.
CAPSTONE PROJECT
The capstone project represents the culmination of candidates' learning, demonstrating mastery of AI techniques. As an AI-enabled business leader, the task is to analyze market opportunities, propose a business plan for an innovative AI product, and present a comprehensive plan including market analysis, product requirements (data & personnel), implementation roadmap, financial projections, and business impact assessment.
CERTIFICATE OF COMPLETION FROM THE UNIVERSITY OF TEXAS AT AUSTIN and 5.0 Continuing Education Units (CEUs)
Upon completing the course successfully, candidates receive a certificate from the esteemed University of Texas at Austin.
ON-CAMPUS IMMERSION IN DECISION SCIENCE AND AI (OPTIONAL PAID PROGRAM)
The Decision Science and AI is a 3-day on-campus Program that presents a valuable opportunity to explore AI use cases and become a driving force behind AI-driven initiatives within your organization. It comprises of dynamic discussions, collaboration with like-minded professionals, and engaging networking sessions hosted at the prestigious University of Texas at Austin.
- Welcome & Program Orientation
- Introduction to AI Network Models- NNs to LLMs
- Campus Tour & Group Photo
- Large Language Models Deep Dive: How do they work & how do we work with them?
- Designing Prompts for one-off Business Problems
- Introduction to Transformers
- RAG & Vector Databases
- RAG system- Q&A bot- Hands-on (Financial Filing of a public company)
- The Art and Science of Negotiations
- Project Brief and Active group work
- Group work on Project
- Group Departs from Austin Campus After Lunch
