Schedule

Readings in normal font should be completed and annotated ahead of lecture.
Readings in italic provide optional additional depth on the material.
Assignments are listed on the day when we suggest you begin working on them.
Assessments are listed on the day when they will occur.

Reading sources:

CN: Class notes written for this course, hosted here.
PDSH: The Python Data Science Handbook by Vanderplas (2016).
PDA: Python for Data Analysis, 3rd edition by McKinney (2022).
BHN: Fairness and Machine Learning: Limitations and Opportunities by Barocas, Hardt, and Narayanan (2023).

Week 1

Mon Jan. 05	Welcome!
Mon Jan. 05	Course introduction. We get started with Python notebooks, the Python data science ecosystem and the Python NumPy library.
	Objectives Getting Oriented Navigation	Reading Course syllabus	Notes Welcome slides Class notes Live notes In-class slides	Assignments Working with NumPy
Tue Jan. 06	Python data science ecosystem: Data frames
Tue Jan. 06	We introduce data wrangling with data frames and the Python Pandas library.
	Objectives Navigation	Reading PDSH: Chapters 1-3	Notes Class notes Live notes In-class slides	Assignments Working with Pandas
Wed Jan. 07	Python data science ecosystem: Visualization
Wed Jan. 07	Data visualization with Python Matplotlib and Seaborn libraries.
	Objectives Navigation	Reading PDSH: Chapter 4	Notes Class notes Live notes In-class slides	Assignments Visualization with Python	Assessments Quiz 1: NumPy, Pandas
Thu Jan. 08	Python data science ecosystem: Data wrangling
Thu Jan. 08	Data wrangling with Python data science tools.
	Objectives Navigation	Reading PDA: Chapters 6-8	Notes Class notes Live notes In-class slides	Assignments Data Wrangling with Python

Week 2

Mon Jan. 12	Introducing Prediction
Mon Jan. 12	Predictive modeling and elementary linear regression
	Objectives Theory Experimentation	Reading PDSH: Chapter 5.1 -- What Is Machine Learning?	Notes Class notes Live notes	Assignments Score Functions Grid Search vs. Optimization	Assessments Quiz 2: Visualization, Data wrangling
Tue Jan. 13	Feature Maps and Overfitting
Tue Jan. 13	How to model nonlinear trends, and the perils of overfitting.
	Objectives Theory Experimentation	Reading PDSH: Chapter 5.6 -- Linear Regression	Notes Class notes Live notes	Assignments Bias in a medical risk predictor
Wed Jan. 14	Classification and Logistic Regression
Wed Jan. 14	Training algorithms that can predict categorical outcomes or guide yes/no decisions.
	Objectives Theory Experimentation	Reading Jurafsky + Martin's notes on logistic regression, sections 5.1-5.3	Notes Class notes Live notes	Assignments Decision Theory in Classification	Assessments Quiz 3: Predictive ML, Feature Maps, Overfitting
Thu Jan. 15	Bias and Fairness in Automated Classification
Thu Jan. 15	A famous, complex case study in algorithmic bias.
	Objectives Social Responsibility Theory Experimentation	Reading Machine Bias	Notes Class notes Live notes	Assignments Tradeoffs Between Conceptions of Fairness

Week 3

Mon Jan. 19	No class -- MLK Day
Mon Jan. 19	Enjoy the holiday!

Tue Jan. 20	Neural Networks for Regression and Classification
Tue Jan. 20	Introducing neural networks, which learn complex feature maps from data.
	Objectives Theory Experimentation	Reading Jurafsky + Martin's notes on neural networks, sections 6.1-6.2	Notes In-class slides Class notes Live notes	Assignments Training Neural Networks	Assessments Written Exam
Wed Jan. 21	Introduction to generative models
Wed Jan. 21	Introduction to generative models via application of GPT2.
	Objectives Navigation, Experiment		Notes In-class slides Class notes Live notes	Assignments Using Generative Models	Assessments Oral Exam
Thu Jan. 22	Generation with Markov Models
Thu Jan. 22	N-gram Markov Models and tokenization for text generation.
	Objectives Navigation, Experiment		Notes Class notes Live notes	Assignments Project Proposal Project Proposal Template	Assessments Oral Exam

Week 4

Mon Jan. 26	Generative neural networks
Mon Jan. 26	Transformer architectures and self-attention
	Objectives Navigation, Experiment	Reading Illustrated Transformer	Notes In-class slides Class notes Live notes
Tue Jan. 27	Implications of GenAI and LLMs
Tue Jan. 27	The societal implications of AI, including current and future risks, benefits and responsible development.
	Objectives Social Responsibility	Reading ChatGPT Isn't 'Hallucinating.' It's Bullshitting. Bender et al., On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?. ACM FAccT Conf. 2021.	Notes In-class slides
Wed Jan. 28	Poster Session
Wed Jan. 28	Class poster sessions presenting your final projects.
	Objectives Project			Assignments Project (Initial due date)
Thu Jan. 29	Poster revision
Thu Jan. 29	Workshop day for revising your final projects based on initial feedback.
	Objectives Project

References

Barocas, Solon, Moritz Hardt, and Arvind Narayanan. 2023. Fairness and Machine Learning: Limitations and Opportunities. Cambridge, Massachusetts: The MIT Press.

McKinney, Wes. 2022. Python for Data Analysis. 3rd ed. O’Reilly Media. https://wesmckinney.com/book/.

Vanderplas, Jacob T. 2016. Python Data Science Handbook: Essential Tools for Working with Data. First edition. Sebastopol, CA: O’Reilly Media, Inc. https://jakevdp.github.io/PythonDataScienceHandbook.