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Lecture Slides

ISLR = Introduction to Statistical Learning PDS = Python for Data Science (see homepage for links)

1. Week 1
   • September 3
     • Intro to Data Science
     • Data Transformation
   • September 5,
     • Tidy Data
     • Data Visualization
     • Tidy Intro, Tidy Advanced, and Vis notebooks
2. Week 2
   • September 10
     • Function Fitting Intro
     • code examples
     • Reading: ISLR 2.1, 3.2.1, 3.5
   • September 12
     • Function Fitting Algorithms
     • demo
     • Reading: ISLR 3.2.1, 3.3.1, 3.3.2, 3.5, 4.3, 7.2, 8.1
3. Week 3
   • September 17
     • Function Fitting Implementation
     • Reading: PDS pg. 390 - 396, 400 - 405 and ISLR 4.6.6
     • Optional reading: ISLR 8.3.1, 8.3.2
     • Notebooks: Regression, knn + logistic regression, and trees
   • September 19
     • Unsupervised Learning Algorithms
     • Reading: ISLR 10.1, 10.2, PDS pg. 462 - 476
     • Optional Reading: ISLR 10.3
4. Week 4
   • September 24
     • Cross Validation + Model Selection
     • Reading: ISLR 5.1, PDS pg. 359 - 375
   • September 26
     • Introduction to Inference
     • Reading: ISLR 3.1.1, 3.1.2, Bayesian Basics (intro - regression models)
     • Optional reading: MSMB 6.1 - 6.6, Statistics for Hackers
5. Week 5
   • October 1
     • The Bootstrap
     • Reading: ISLR 5.2 - 5.3
     • Optional reading: CASI Chapter 1
   • October 3
     • Large Scale Inference and Experimental Design
     • Reading: MSMB 6.7 - 6.11, 13.1 - 13.4.
     • Optional reading: CASI 15.1 - 15.3
6. Week 6
   • October 8
     • Feature engineering + Outlier and error analysis
     • Reading: PDS 3
   • October 10
     • Feature selection
     • the slides that are covered in the class are: slides 1 - 17 and 30 - 59
     • Reading: An Introduction to Variable and Feature Selection, KDD tutorial
     • Optional reading: Feature Selection for Data and Pattern Recognition, Computational Methods of Feature Selection, A Survey of Feature Selection Techniques
     • Feature selection tool scikit-learn, featureminer
7. Week 7 (Invited Guest Lectures)
   • October 15
     • Geospatial and Time series data analysis by dr. Behrouz Babaki
     • Optional reading: Python Geospatial Development
     • Tools: Shapely, Basemap, geopandas
   • October 17
     • Privacy and Transparency in Machine Learning by dr. Ulrich Aivodji
     • Optional reading: Composition Attacks and Auxiliary Information in DataPrivacy, A Survey Of Methods For Explaining Black Box Models, Privacy risk in machine learning- Analyzing the connection to overfitting, On the Protection of Private Information in Machine Learning Systems-Two Recent Approaches, fairwashing-the risk of rationaliztion
     • Protecting Privacy with MATH: Video
8. Week 8
   • October 22
     • Reading week
9. Week 9
   • October 31
     • MIDTERM: 16:30-18:30 at Z-110
10. Week 10
    • November 5
      • Project presentation *
    • November 7
      • Algorithmic bias: Source and types of data bias, bias and discrimination in machine Learning: fairness metrics
      • Reading: Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries, Fairness definitions explained
      • Video: The Trouble with Bias, 21 fairness definitions and their politics
      • Fairness metric tools: The Aequitas Toolkit Paper, The Aequitas Tool
11. Week 11
    • November 12
      • Text mining (NLP): part 1: NLP pipeline, BOW, n-grams, TF-IDF, and language model
      • Reading: Speech and Language Processing, Jurafsky and Martn, 2nd ed. (plus): Chapters 1 - 6
      • Tools: Python: NLTK, Gensim, Java: Stanford CoreNLP, Apache OpenNLP
    • November 14
      • Text mining (NLP): part 2: Word embeddings, SVD, Word2Vec and GLoVe.
      • Reading: Word2Vec, GLoVe
      • Computer Vision: part 1: CV pipleline, Warping, Point processing, Filters
      • Reading: Computer Vision:Algorithms and Applications, Richard Szeliski, Chapters 3.1.1, 3.2, 3.6.1
      • Tools: Python: OpenCV
12. Week 12
    • November 19
      • Computer Vision: part 2: Convolution
      • Reading: Computer Vision:Algorithms and Applications, Richard Szeliski, Chapters 3.1.1, 3.2, 3.6.1, Filtering, Convolution, Correlation, Chapter 5
    • November 21
      • Crash course to deep learning: Perceptrons, Neural networks, Convolution Neural networks, Recurrent Neural Network and LSTM
      • Reading: Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, Chapter 6, 9, 10
      • ConvNet notes
      • Tools: Tensorflow, Keras, Pytorch
13. Week 13
    • November 26
      • Graph ML: part 1: node embeddings: adjajency matrix,matrix factorization, multi-hop embedding, random walk embeddings, and node2vec
      • Reading: Representation learning on graphs
      • Tools: Python: igraph, NetworkX
      • Tool: embedding tools: node2vec, deepwalk, GraphVite
    • November 28
      • Graph ML: part 2: Graph neural networks, and Graph convolutional networks
      • Multimodal Learning: Multimodal machine learning, Multimodal Representation, Fusion
      • Tools: Deep autoencoders in Keras, Ensemble methods: scikit-learn
      • Reading: Multimodal Machine Learning: A Survey and Taxonomy
14. Week 14
    • December 3
      • Final project presentation
15. Week 15
    • December 10
      • FINAL: 16:30-18:59 at Z310 and Z330