Date 
Lecture 
Topics 
Readings and useful links 
Handouts 
Jan 17 
Course Overview

 Intro, admin
 Machine Learning Examples
 Decision Tree Learning


Slides

Jan 22 
Decision Tree Learning

 Decision Tree Learning
 The Big Picture
 Overfitting

 Mitchell Chapters 1,2,6.16.3
 Murphy Chapter 2
 Bishop Chapter 1,2

Slides

Jan 24 
Learning Linear Separators

 Learning Linear Separators
 The Perceptron Algorithm
 Margins

 Mitchell Chapters 4.1.2 and 4.4.1
 Bishop Chapter 4.1.7
 Daume: The Perceptron

Slides

Jan 29 
Estimating Probabilities from Data 

Mitchell: Estimating Probabilities

Slides

Jan 31 
Naive Bayes 
 Conditional Independence
 Naive Bayes: Why and How

Mitchell: Naive Bayes and Logistic Regression

Slides

Feb 5 
Naive Bayes 
 Naive Bayes: Why and How
 Bag of Words

Mitchell: Naive Bayes and Logistic Regression

Slides

Feb 7 
Logistic Regression 
 Logistic Regression: Maximizing Conditional Likelihood
 Gradient Descent


Slides

Feb 12 
Logistic Regression 


Slides

Feb 14 
Application Area: Computer Vision 
 Problems and Challenges in Computer Vision
 Deep Learning in Computer Vision

Lectures 811 from Jitendra Malik's course on computer vision

Slides

Feb 19 
Kernels 
 Kernels
 Kernelizing Algorithms
 Kernelizing Perceptron

Bishop 6.16.2 
Slides

Feb 21 
Support Vector Machines 
 Geometric Margins
 SVM: Primal and Dual Forms
 Kernelizing SVM

Notes on SVM by Andrew Ng

Slides

Feb 26 
Generalization and Overfitting 
 Sample Complexity
 Finite Hypothesis Classes

Mitchell: Ch 7
Notes on Generalization Guarantees

Slides

Feb 28 
Generalization and Overfitting 
 Sample Complexity
 VC Dimension Based Bounds

Mitchell: Ch 7
Notes on Generalization Guarantees

Slides

Mar 5 
Model Selection, Regularization 


Slides

Mar 7  Midterm

Mar 1216  No Class: Midsemester Break 
Mar 19 
Model Selection, Regularization 
 Structural Risk Minimization
 Regularization
 kFold Cross Validation


Slides
Slides 
Mar 21 
Linear Regression 
 Linear Regression
 Minimizing squared error and maximizing data likelihood

Murphy: Chapter 7.17.3 
Slides

Mar 26 
Neural Networks 
 Neural Networks
 Backpropagation

Mitchell: Chapter 4

Slides

Mar 28 
Deep Networks 
 Convolution
 Convolutional Neural Networks
 LeNet5 Architecture

Goodfellow: Chapter 9

Slides

Apr 2 
Boosting 
 Boosting Accuracy
 Adaboost


Slides

Apr 4 
Unsupervised Learning 
 Objective Based Clustering
 Hierarchical Clustering

Hastie, Tibshirani and Friedman, Chapter 14.3
Center Based Clustering: A Foundational Perspective

Slides

Apr 9 
 Learning Representations
 Dimensionality Reduction

 Hierarchical Clustering
 PCA
 Dimensionality Reduction

Bishop 12.1, 12.3

Slides
Slides

Apr 11 
Interactive Learning 
 Active Learning
 Common heuristics, Sampling bias
 Safe Disagreement Based Active Learning Schemes

Two Faces of Active Learning by Sanjoy Dasgupta

Slides
Slides

Apr 16 
Active Learning, SemiSupervised Learning 
 SemiSupervised Learning
 Transductive SVM
 Cotraining

SemiSupervised Learning in Encyclopedia of Machine Learning, Jerry Zhu

Slides
Slides

Apr 18 
Reinforcement Learning 
 Markov Decision Processes
 Value Iteration
 QLearning


Slides

Apr 23 
Project Presentations 



Apr 25 
Project Presentations 



Apr 30 
Recap 



May 2  Final 