95-865: Unstructured Data Analytics
(Spring 2025 Mini 4)

Unstructured Data Analytics

Lectures:
Note that the current plan is for Section B4 to be recorded.

There is also a Z4 section that is only for Heinz students in a part-time distance learning degree program, which follows a different format of the course but uses recordings of lectures and recitations (the Z4 section is not open to on-campus full-time CMU students).

Recitations (A4/B4): Fridays 5pm-6:20pm, HBH A301

Instructor: George Chen (email: georgechen ♣ cmu.edu) ‐ replace "♣" with the "at" symbol

Teaching assistants:

Office hours (starting second week of class): Check the course Canvas homepage for the office hour times and locations.

Contact: Please use Piazza (follow the link to it within Canvas) and, whenever possible, post so that everyone can see (if you have a question, chances are other people can benefit from the answer as well!).

Course Description

Companies, governments, and other organizations now collect massive amounts of data such as text, images, audio, and video. How do we turn this heterogeneous mess of data into actionable insights? A common problem is that we often do not know what structure underlies the data ahead of time, hence the data often being referred to as "unstructured". This course takes a practical approach to unstructured data analysis via a two-step approach:

  1. We first examine how to identify possible structure present in the data via visualization and other exploratory methods.
  2. Once we have clues for what structure is present in the data, we turn toward exploiting this structure to make predictions.
Many examples are given for how these methods help solve real problems faced by organizations. Along the way, we encounter many of the most popular methods in analyzing unstructured data, from modern classics in manifold learning, clustering, and topic modeling to some of the latest developments in deep neural networks for analyzing text, images, and time series.

We will be coding lots of Python and dabble a bit with GPU computing (Google Colab).

Note regarding GenAI (including large language models): As likely all of you are aware, there are now technologies like (Chat)GPT, Gemini, Claude, Llama, DeepSeek, etc which will all be getting better over time. If you use any of these in your homework, please cite them. For the purposes of the class, I will view these as external collaborators (no different than if you got help from a human friend). For exams, I want to make sure that you actually understand the material and are not just telling me what someone else or an AI assistant knows. This is important so that in the future, if you get help from an AI assistant (or a human) to aid you in your unstructured data analysis, you have enough background knowledge to check for yourself whether you think the AI (or human) is giving you a solution that is correct or not. For this reason, exams in this class will explicitly not allow electronics.

Prerequisite: If you are a Heinz student, then you must have taken 95-888 "Data-Focused Python" or 90-819 "Intermediate Programming with Python". If you are not a Heinz student and would like to take the course, please contact the instructor and clearly state what Python courses you have taken/what Python experience you have.

Helpful but not required: Math at the level of calculus and linear algebra may help you appreciate some of the material more

Grading: Homework (30%), Quiz 1 (35%), Quiz 2 (35%*)

*Students with the most instructor-endorsed posts on Piazza will receive a slight bonus at the end of the mini, which will be added directly to their Quiz 2 score (a maximum of 10 bonus points, so that it is possible to get 110 out of 100 points on Quiz 2).

Letter grades are determined based on a curve.

Calendar for Sections A4/B4 (tentative)

Previous version of course (including lecture slides and demos): 95-865 Fall 2024 mini 2

Date Topic Supplemental Materials
Part I. Exploratory data analysis
Week 1
Mon Mar 10 Lecture 1: Course overview, analyzing text using frequencies
[slides]

Please install Anaconda Python 3 and spaCy by following this tutorial (needed for HW1 and the demo next lecture):
[slides]
Note: Anaconda Python 3 includes support for Jupyter notebooks, which we use extensively in this class

Tue Mar 11 HW1 released
Wed Mar 12 Lecture 2: Basic text analysis demo (requires Anaconda Python 3 & spaCy)
[slides]
[Jupyter notebook (basic text analysis)]
Fri Mar 14 Recitation slot: Lecture 3 — Basic text analysis (cont'd), co-occurrence analysis
[slides]
[Jupyter notebook (basic text analysis using arrays)]
[Jupyter notebook (co-occurrence analysis toy example)] 		As we saw in class, PMI is defined in terms of log probabilities. Here's additional reading that provides some intuition on log probabilities (technical):
[Section 1.2 of lecture notes from CMU 10-704 "Information Processing and Learning" Lecture 1 (Fall 2016) discusses "information content" of random outcomes, which are in terms of log probabilities]
Week 2
Mon Mar 17 Lecture 4: Co-occurrence analysis (cont'd), visualizing high-dimensional data with PCA
[slides]
[Jupyter notebook (text generation using n-grams)]
[Jupyter notebook (PCA)] 		Additional reading (technical):
[Abdi and Williams's PCA review]

Supplemental videos:
[StatQuest: PCA main ideas in only 5 minutes!!!]
[StatQuest: Principal Component Analysis (PCA) Step-by-Step (note that this is a more technical introduction than mine using SVD/eigenvalues)]
[StatQuest: PCA - Practical Tips]
[StatQuest: PCA in Python (note that this video is more Pandas-focused whereas 95-865 is taught in a manner that is more numpy-focused to better prep for working with PyTorch later)]
Wed Mar 19 Lecture 5: PCA (cont'd), manifold learning (Isomap, MDS)
[slides]
[the first demo is actually wrapping up the PCA demo from the previous lecture: Jupyter notebook (PCA)]
[Jupyter notebook (manifold learning)] 		Additional reading (technical):
[The original Isomap paper (Tenenbaum et al 2000)]

Python examples for manifold learning:
[scikit-learn example (Isomap, t-SNE, and many other methods)]

Supplemental video:
[StatQuest: t-SNE, clearly explained]

Additional reading (technical):
[some technical slides on t-SNE by George for 95-865]
[Simon Carbonnelle's much more technical t-SNE slides]
[t-SNE webpage]
Fri Mar 21 Recitation slot: More on dimensionality reduction
[slides (how to save a Jupyter notebook as PDF)]
[Jupyter notebook (more on PCA, argsort)]
["How to Use t-SNE Effectively" (Wattenberg et al 2016)]
[Jupyter notebook (analyzing the 20 Newsgroups dataset)]
Week 3
Mon Mar 24 HW1 due Monday Mar 24, 11:59pm

Lecture 6: Wrap up manifold learning, intro to clustering
[slides]
[Jupyter notebook (PCA and t-SNE with images)***]
***For the demo on PCA and t-SNE with images to work, you will need to install some packages:
pip install torch torchvision 		New manifold learning method that is promising (PaCMAP):
[paper (Wang et al 2021) (technical)]
[code (github repo)]

Additional reading on clustering(technical):
[see Section 14.3 of the book "Elements of Statistical Learning"]

Wed Mar 26 Lecture 7: Clustering
Fri Mar 28 Recitation slot: Quiz 1 (80-minute exam) — material coverage is up to and including Mon Mar 24's lecture (i.e., Lecture 6)
Week 4
Mon Mar 31 Lecture 8: Clustering (cont'd)
Wed Apr 2 Lecture 9: Wrap up clustering, topic modeling
Fri Apr 4 No class (CMU Spring Carnival) 🎪
Part II. Predictive data analysis
Week 5
Mon Apr 7 Lecture 10: Intro to predictive data analysis
Wed Apr 9 Lecture 11: wrap up intro predictive data analysis; intro to neural nets & deep learning
Fri Apr 11 Recitation slot: Some key concepts for prediction
Week 6
Mon Apr 14 HW2 due Monday Apr 14, 11:59pm

Lecture 12: Wrap up neural net basics; image analysis with convolutional neural nets (also called CNNs or convnets)
Wed Apr 16 Lecture 13: Time series analysis with recurrent neural nets (RNNs)
Fri Apr 18 Recitation slot: TBD
Week 7
Mon Apr 21 Lecture 14: Text generation with generative pretrained transformers (GPTs)
Wed Apr 23 Lecture 15: Other deep learning topics; course wrap-up
Fri Apr 25 Recitation slot: TBD
Final exam week
Mon Apr 28 HW3 due 11:59pm
Fri May 2 Quiz 2 (80-minute exam) — 1pm-2:20pm, location TBD

Quiz 2 focuses on material from Wed Mar 26's lecture (Lecture 7) and onwards (note that by how the course is set up, material from Lecture 7 onwards naturally at times relates to material from Lectures 1–6, so some ideas in these earlier lectures could still possibly show up on Quiz 2—please focus your studying on material from Lecture 7 onwards)