A comprehensive AI program spanning AI fundamentals to advanced AI agent applications. Designed by UT Austin McCombs faculty and industry experts.
Key AI terminology (AI, ML, Deep Learning, GenAI, LLMs), history & evolution of AI, business problems across industries, Google Colab setup, Python variables, data types, data structures, conditional statements.
Read, explore, manipulate, and visualize data. Exploratory data analysis using Python packages to solve business problems and deliver actionable insights.
Weeks 1-4 · Intro to Python → Data Manipulation → EDA → Project
Learning from data, linear & non-linear models to capture relationships between attributes and outcomes, discovering patterns in unlabeled data through segmentation.
Weeks 5-9 · Linear Regression → Decision Trees → K-means Clustering → Project → Break
Combining decisions from multiple models using ensemble techniques to improve performance. Feature engineering and hyperparameter tuning to build generalized, robust models that optimize business costs.
Weeks 10-13 · Bagging → Boosting → Model Tuning → Project
Implement neural networks, optimization algorithms, regularization techniques, and evaluate factors that improve performance to build generalized and robust models.
Weeks 14-16 · Intro to Neural Networks → Optimizing NNs → Project
NLP fundamentals, text embedding methods, transformer architectures powering LLMs, Retrieval-Augmented Generation (RAG), prompt engineering with open-source LLMs.
Weeks 17-22 · Word Embeddings → Attention & Transformers → LLMs & Prompt Engineering → RAG → Project → Break
Build intelligent agents using LangChain, tool-use capabilities, memory integration, planning, multi-step reasoning, ReAct framework, and agent evaluation with human oversight.
Weeks 23-26 · AI Agent Workflows → Planning & Reasoning → Evaluating Agents → Project
Understand the role of deployment in realizing ML value. Build and deploy applications using Python and containerization.
Weeks 27-29 · Intro to Deployment → Containerization → Project
Code generation, text-to-image & image-to-text with DALL-E, speech recognition with Whisper, and practical GenAI business applications.
Image processing, extracting features from images, building CNNs to uncover hidden patterns and solve image classification problems.
Statistical analysis with Python — confidence intervals, hypothesis testing, data distributions, sampling, central limit theorem, estimation theory.
Popularity-based models, collaborative filtering, content-based systems, and hybrid recommendation approaches.
DBMS fundamentals, normalization, joins, sorting, set operations, grouping, and filtering.