10601, Spring 2017
School of Computer Science
Carnegie Mellon University
This schedule is tentative and subject to change. Please check back often.
Date  Lecture  Readings  Announcements  

MC  HTF  MP  BI  Other  
Wed, 18Jan  Lecture 1
:
Course Overview [Slides] [Video] 
1  1, 2  1  1 



Classification and Regression 

Mon, 23Jan  Lecture 2
:
Machine Learning in Practice / kNearest Neighbors [Slides] [Whiteboard] [Video] 
8.2  13.3    2.5.2 



Tue, 24Jan 
Background Test (Evening) 


Wed, 25Jan  Lecture 3
:
Experimental Design / kNearest Neighbors [Slides] [Whiteboard] [Video] 
       



Mon, 30Jan  Lecture 4
:
The Probabilistic Approach to Learning from Data [Slides] [Whiteboard] [Video] 
    2  2 



Wed, 1Feb  Lecture 5
:
MLE and MAP / Naive Bayes [Slides] [Whiteboard] [Video] 
6.16.10    3   

HW1 due 

Mon, 6Feb  Lecture 6
:
Gaussian Naive Bayes [Slides] [Whiteboard] [Video] 
       



Wed, 8Feb  Lecture 7
:
Optimization for ML / Linear Regression [Slides] [Whiteboard] [Video] 
       



Mon, 13Feb  Lecture 8
:
Linear Regression [Slides] [Whiteboard] [Video] 
  3.13.4  7.17.3  3.1 

HW2 due 

Wed, 15Feb  Lecture 9
:
Logistic Regression / Nonlinear features [Slides] [Whiteboard] [Video] 
  4.1, 4.4  8.18.3, 8.6  4.3.2, 4.3.4 



Mon, 20Feb  Lecture 10
:
Regularization / Perceptrons and Large Margin [Slides] [Whiteboard] [Video] 
4.4.0    8.5.4  4.1.7 



Wed, 22Feb  Lecture 11
:
Kernels / Kernel Perceptron / SVMs [Slides] [Whiteboard] [Video] 
    14.1  14.2.4  6.16.2 

HW3 due [Course Survey due Fri, Feb 24] 

Mon, 27Feb  Lecture 12
:
Kernels / SVMs [Slides] [Whiteboard] [Video] 
  12  12.38  14.5  7.1 



Learning Theory 

Wed, 1Mar  Lecture 13
:
Learning Theory (Part I)  Statistical Estimation [Slides] [Whiteboard] [Video] 
7       

[HW4 due Fri, Mar 03] 

Mon, 6Mar  Lecture 14
:
Midterm Exam Review [Slides] [Video] 


Tue, 7Mar 
Midterm Exam (Evening Exam) 7:00pm  9:30pm  see Piazza for details about the location 


Unsupervised Learning 

Wed, 8Mar  Lecture 15
:
Clustering [Slides] [Whiteboard] [Video] 
  14.3.0  25.5  12.1, 12.3 



Mon, 13Mar 
(No class: Midsemester break) 


Wed, 15Mar 
(No class: Midsemester break) 


Mon, 20Mar  Lecture 16
:
KMeans / GMMs [Slides] [Whiteboard] [Video] 
6.12  6.12.2  8.5  8.5.3  11.4.1, 11.4.2, 11.4.4  9 



Wed, 22Mar  Lecture 17
:
Expectation Maximization / PCA and Dimensionality Reduction [Slides] [Whiteboard] [Video] 
6.12  6.12.2  8.5  8.5.3  11.4.1, 11.4.2, 11.4.4  9 

HW5 (Part I) due 

Feature Learning 

Mon, 27Mar  Lecture 18
:
PCA / Neural Networks [Slides] [Whiteboard] [Video] 
  14.5  12  12 



Wed, 29Mar  Lecture 19
:
Neural Networks [Slides] [Whiteboard] [Video] 
4  11    5 



Mon, 3Apr  Lecture 20
:
Backpropagation [Slides] [Whiteboard] [Video] 
       

HW6 due 

Wed, 5Apr  Lecture 21
:
Deep Learning / CNNs [Slides] [Whiteboard] [Video] 
    28   

HW5 (Part II) due 

Graphical Models 

Mon, 10Apr  Lecture 22
:
Bayesian Networks (Part I) [Slides] [Whiteboard] [Video] 
6.11    10  10.2.1  8.1, 8.2.2 



Wed, 12Apr  Lecture 23
:
Bayesian Networks (Part II) [Slides] [Whiteboard] [Video] 
6.11    10  10.2.1  8.1, 8.2.2 



Mon, 17Apr  Lecture 24
:
Hidden Markov Models [Slides] [Whiteboard] [Video] 
    10.2.2  10.2.3  13.113.2 

HW7 due 

Learning Paradigms 

Wed, 19Apr  Lecture 25
:
Matrix Factorization and collaborative filtering [Slides] [Whiteboard] [Video] 
       



Mon, 24Apr  Lecture 26
:
Reinforcement Learning [Slides] [Video] 
13       

HW8 due 

Wed, 26Apr  Lecture 27
:
Information Theory [Slides] [Video] 
7       



Learning Theory 

Mon, 1May  Lecture 28
:
Learning Theory (Part II)  PAC Learning [Slides] [Whiteboard] [Video] 
7       



Wed, 3May  Lecture 29
:
Final Exam Review [Slides] [Whiteboard] [Video] 
HW9 due 

Mon, 8May 
Final exam, 5:30pm  08:30pm 
