For Spring 2020, the course prerequisites have been updated: prior to taking 94775, you must have already taken 95791 "Data Mining" and either 90819 "Intermediate Programming with Python" (new class) or 95888 "Data Focused Python".
Class time and location:
Instructor: George Chen (georgechen [at symbol] cmu.edu)
Teaching assistants: David Pinski (dpinski [at symbol] andrew.cmu.edu), Emaad Manzoor (emaad [at symbol] cmu.edu)
Office hours (starting second week of class):
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!).
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 twostep approach:
Prerequisite: If you are a Heinz student, then you must have either (1) passed the Heinz Python exemption exam, or (2) taken 90812 "Introduction to Programming with Python", 95888 "DataFocused Python", or 16791 "Applied Data Science". 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: HW1 8%, HW2 8%, HW3 4%, midmini quiz 35%, final project proposal 10%, final project 35%. HW3 is shorter than HW1 and HW2. Letter grades are determined based on a curve.
Syllabus: [pdf]
Previous lecture slides and demos: The spring 2017 version of 94775 (click to see course webpage) was quite different and did not have Python as a prerequisite. At this point, the closest material coverage is last mini's 95865 (click to see course webpage).
Date  Topic  Supplemental Material 

Part I. Exploratory data analysis  
Tue Jan 15 
Lecture 1: Course overview, basic text processing and frequency analysis
HW1 released (check Canvas)! 

Thur Jan 17 
Lecture 2: Basic text analysis demo, cooccurrence analysis


Fri Jan 18 
Recitation 1: Basic Python review


Tue Jan 22 
Lecture 3: Finding possibly related entities, PCA

Causality additional reading:
PCA additional reading (technical): 
Thur Jan 24 
Lecture 4: Manifold learning with Isomap and tSNE

Python examples for dimensionality reduction:
Additional dimensionality reduction reading (technical):

Fri Jan 25 
Recitation 2: tSNE
HW1 due 11:59pm, HW2 released 

Tue Jan 29 
Lecture 5: Introduction to clustering, kmeans, Gaussian mixture models

Additional clustering reading (technical): 
Thur Jan 31 
Class cancelled due to polar vortex 

Fri Feb 1 
Lecture 6 (this is not a typo): Clustering and clustering interpretation demo, automatic selection of k with CH index

Additional clustering reading (technical):
Python cluster evaluation: 
Tue Feb 5 
Recitation 3: Quiz review session 

Thur Feb 7  Midmini quiz (same time/place as lecture)  
Fri Feb 8 
Recitation 4: Final project work section 

Tue Feb 12 
Lecture 7: Hierarchical clustering, topic modeling

Additional reading (technical): 
Part 2. Predictive data analysis  
Thur Feb 14 
Lecture 8: Topic modeling (wrapup), introduction to predictive analytics, nearest neighbors, evaluating
prediction methods


Fri Feb 15 
Recitation 5: Support vector machines, decision boundaries, ROC curves
HW2 and final project proposal due 11:59pm, HW3 released 

Tue Feb 19 
Lecture 9: Prediction and model validation demo, decision trees/forests


Thur Feb 21 
Lecture 10: Introduction to neural nets and deep learning
Mike Jordan's Medium article on where AI is at (April 2018): 
Video introduction on neural nets:
Additional reading: 
Fri Feb 22 
Recitation 6: Sentiment analysis, extensions to topic modeling


Mon Feb 25  HW3 due 11:59pm 

Tue Feb 26 
Lecture 11: Image analysis with CNNs (also called convnets)

CNN reading:

Thur Feb 28 
Lecture 12: Time series analysis with RNNs, other deep learning topics, wrapup
Gary Marcus's Medium article on limitations of deep learning and his heated debate with Yann LeCun (December 2018): 
LSTM reading:
Videos on learning neural nets (warning: the loss function used is not the same as what we are using in class):
Recent heuristics/theory on gradient descent variants for deep nets (technical):
Some interesting reads (technical): 
Fri Mar 1 
Recitation 7: Final project work section 

Tue Mar 5 
Final project presentations inclass 

Thur Mar 7 
No class Final projects due 11:59pm (slide deck + Jupyter notebook) 