Overview of Probability& Statistics

  • Overview of machine learning and its applications
  • Probability & Statistics overview
    • Random variables, expectations, distributions, Bayes rule, prior, posterior

Mon., Aug. 24 (Geoff)

  • Lecture:Overview of machine learning and applications [Slides] [Annotated]
  • Lecture:Overview of probability and statistics [Annotated1]
  • In the annotated version, some slides are cutoff. Here we include a version of the same lecture, where there is type setted notes below each slide. [Annotated2]
  • Readings: Bishop pages 1-4 and section 1.2

Wed., Aug. 26 (Geoff):

  • Lecture:Overview of probability and statistics [Slides] [Annotated]
  • Readings: Bishop sections 2.0-2.3

Graphical Models

Mon., Aug.31 (Geoff)

Wed., Sep.2 (Geoff)

Wed., Sep.9 (Geoff)

  • Lecture: Graphical models continued. [Slides] [Annotated]
  • Code to plot the two dimensional continous probability function that was shown in the lecture:
  • code
  • Readings: Bishop pages 359-362 (sec 8-8.1),365-369 (sec 8.1.2-8.1.3),372-383 (sec 8.2),393-402 (sec 8.4-8.4.3),optional: sec 8.3,8.4.4

Density Estimation

Wed., Sep.9 (Miro)

  • Lecture: Density estimation [Slides]
  • Readings: Bishop sections 1.2.3, 2.0, 2.1

Mon., Sep.14 (Miro)

  • Lecture: Density estimation [Slides]
  • There is a correction in slides the corrected version is [Annotated]

Regression

Wed., Sep.16 (Miro)

  • Lecture: Gaussians and Regression [Annotated]
  • Readings: Bishop pages 78-79 (sec 1.2.4, 2.3.0), 97-98 (sec 2.3.4, 2.3.6),4-9 (sec 1.1), 138-140 (sec 3.1.0).

Mon., Sep. 21 (Miro)

  • Lecture: Regression and model selection [Annotated]
  • Readings: Bishop pages 10-12 (sec 1.1) and sections 1.2.5, 3.1.1, 3.1.2, 3.1.4, 3.2

Classification

Wed., Sep. 23 (Miro)

  • Lecture: Model selection and Naive Bayes [Annotated]
  • Readings: Bishop sections 1.3, 1.5 Tom Mitchell's Chapter on Naive Bayes (Sections 1 and 2) can be accessed here

Mon., Sep. 28 (Geoff)

Wed., Sep. 30 (Geoff)

Approximate Inference

Mon., Oct. 5 (Geoff)

Wed., Oct. 7 (Geoff)

Latent Variables Analysis

Mon., Oct. 12 (Geoff)

  • Lecture: Sampling continued and Principal Component Analysis [Slides] [Annotated]
  • Readings: Bishop pages 559-577.

Wed., Oct. 14 (Geoff)

  • Lecture: Latent variable models continued [Slides] [Annnotated
  • Readings: Hastie-Tibshirani-Friedman The Elements of Statistical Learning 2nd edition 14.5 (PCA, principal curves) and 14.10 (PageRank), accesible here.

Mon., Oct. 19 (Geoff + Miro)

  • Lecture: Bayesian PCA [Slides] [Annnotated]
  • Lecture: k-Nearest Neighbor Decision Trees [Slides]
  • Readings: k-Nearest Neighbor Classifiers: Bishop Figures 2.27, 2.28,Hastie-Tibshirani-Friedman Section 2.3.2.

Decision Trees

Wed., Oct. 21 (Miro)

  • Lecture: Decision Trees [Slides]
  • Readings: Bishop Sections 1.6, 14.4

Learning Theory

Mon., Oct. 24 (Miro)

Online learning and Ensemble Learning

Wed., Nov. 4 (Miro)

  • Lecture:Online learning and ensemble learning [Slides]
  • Readings:
  • Randomized weighted majority and perceptron (optional reading): Notes
  • Ensemble learning: Paper
  • Bagging & random forests (optional reading): Section 8.7 and beginning of Section 5 (pages 587-592) of Hastie, Tibshirani, Friendman, 2nd ed: Book

Mon., Nov. 9 (Miro)

Support Vector Machines

Wed., Nov. 11 (Miro)

  • Lecture: Support Vector Machines [Slides]
  • Readings: Section 3.1 of Chris Burges tutorial on SVMs: PDF

Mon., Nov. 16 (Miro)

  • Lecture: Support Vector Machines continued. [Slides]

Wed., Nov 18 (Miro)

Clustering

Mon., Nov 23 (Geoff)

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