CSE517a Calendar (SP19)

This is an inactive course webpage.

All course information and contents for SP2020 will be available on Canvas.

 

If you are wondering where we are going: here is a Roadmap!

(*) indicates optional more advanced reading for the interested student

_______

Topic

Reading

15 Jan

17 Jan

Course Overview, Syllabus

Structural Risk Minimization

  • lecture notes (.tex, .pdf)
    • HERE is a folder with the images (remember to update this folder as we add new illustrations)
  • FCML: Ch1, Linear Modelling
  • ESL: 3.4.3, 10.6
22 Jan  Optimization

  • GD (brief recap)
  • Newton
  • SGD
  • momentum method
  • LFD: 3.3.2, Gradient Descent
  • FCML: Comments 4.1 & 2.6
24 Jan Estimating Probabilities form Data

  • Coin Flipping
  • MLE
  • MAP
  • FCML: 2.1-2.6, Random Variables and Probability
  • FCML: 3.1-3.7, Coin Game
29 Jan  MLE and MAP for discriminative ML

  • Linear Regression
  • Ridge Regression
  • Logistic Regression
  • FCML: 2.8, MLE
  • FCML: 3.8, 4.2-4.3, MAP
  • FCML: 5.2.2, Logistic Regression
31 Jan   Squared Euclidean Distances

  • Use-cases
  • Matrix Equations
  • Efficient Computation
5 Feb

7 Feb

 Naive Bayes

  • Generative ML
  • Categorical features
  • Multinomial features
  • Continuous features (Gaussian NB)
  • Missing features
  • FCML: 5.2.1, Bayes Classifier and NB
  • Ch 3 of Tom Mitchell’s ML book
12 Feb

14 Feb

 Performance Evaluation

  • Performance Measures
    • Regression
    • Classification
  • Statistical Tests
  • Cross-Validation
  • Re-sampling
  • lecture notes (.tex, .pdf, slides)
  • FCML: 5.4, Performance
  • ESL: 7.10, Cross-Validation
19 Feb  RBF Networks

  • Radial basis functions
  • Kernel regression
  • RBF network
  • lecture notes (.tex, .pdf)
  • LFD: eCh6.3-6.3.2, RBF Networks
21 Feb

26 Feb

 Kernels

  • Valid Kernels
  • Kernel Construction
  • Kernel Machines
  • LFD
    • 3.4, Non-linear Transformation
    • 8.3, Kernel Trick & Kernel SVM
  • FCML: 5.3.2, SVM and Kernel Methods
28 Feb  Recitation Session

  • features for text data and text classification
  • discussion of written hw1 and hw2 (selected problems)
For questions contact Zach and Trevor.

Mar 5

Mar 7

 Gaussian Processes

  • GPR via Weight-space View
  • Definition: GP
  • GPR via Function-space View
    • Noise-free Observations
    • Noisy Observations
    • noisy Predictions
  • GPR Algorithm
  • Hyperparameter Learning
  • lecture notes (.tex, .pdf)
  • FCML: 8.1, Non-Parametric Models 
  • FCML: 8.2, GP Regression 
  • GPML: 2.2-3, Function-space View
  • (*) GPML: 2.1, Weight-space View
  • (*) GPML: 4.2, Covariance functions
  • (*) GPML: 5, Hyperparameter Learning & Model selection
Mar 12

Mar 14

 Spring Break
Mar 19 MIDTERM EXAM

  • in-class
You can bring a cheat sheet with the following specifications:

  • one US-letter sized page
  • double-sided
  • handwritten
Mar 21 Wrap-up: Kernel Methods and GPs

  • GP Hyperparameter Training
  • Multi-class Classification (for SVM, GPs)
    • 1-vs-all
    • Platt scaling
    • 1-vs-1
    • (*) Multi-class Logistic Regression
  • Application: Traffic Prediction
  • FCML: 8.4, Hyperparameter Optimisation
  • GPML: 5.1, 5.3-4  Hyperparameter Learning & Model selection 
Mar 26

Mar 28

 Clustering 

  • Unsupervised Learning
  • k-Means
  • Kernel k-Means
  • GMMs
  • lecture notes (.tex, .pdf)
  • FCML: 6., Intro
  • FCML: 6.2, k-Means, Kernel k-Means
  • FCML: 6.3.1-7, Mixture Models 
Apr 2

Apr 4

Dimensionality Reduction

  • PCA/SVD/MDS
  • Non-linear Dimensionality Reduction
  • Data Preprocessing
  • Feature Engineering
  • lecture notes (.tex, .pdf)
  • FCML: 7.1-7.2, PCA
  • LFD:
    • eCH 9.1, Input Preprocessing
    • eCH 9.2, PCA, SVD
  • slides
Apr 9

Apr 11

Neural Networks – Basics

  • Examples
  • Feed-Forward NNs
  • Back-Propagation
Apr 16

Apr 18

Learning (Deep) NNs

  • Architecture
  • Regularization
  • Optimization
  • Initialization
  • Pre-training

 

Apr 23 Beyond FFNNs

  • Autoencoders
  • Recurrent NNs
  • [optional] Convolutional NNs
Apr 25  Semi-Supervised Learning 

  • self-training & co-training
  • GMMs
  • graph-based SSL
  • [optional] generative models
  • [optional] S3VM
  • (*) ESL: 17.1-17.3.1, Undirected Graphical Models
7 May Final EXAM

  • 6-7pm (starts 6pm sharp!)
    • be there by 5:50pm
  • in Louderman 458
You can bring a cheat sheet with the following specifications:

  • one US-letter sized page
  • double-sided
  • handwritten

(*) indicates more advanced reading for the interested student