View source for Draft schedule for 10-601B in Spring 2016

This is '''NOT''' the syllabus for [[Machine Learning 10-601 in Spring 2016]].  It's just a draft....

=== Schedule ===

{| border="1"
|+ Schedule for 10-601 
!  Date 
!  Main Topic of Lecture
!  Lecturer 
!  Assignment
!  TAs
|-
| M 1/11 || [http://www.cs.cmu.edu/~wcohen/10-601/nina/intro-ml-601.pdf Course Overview]  ||  Nina  || || 
|-
| W 1/13 || [[10-601 Introduction to Probability|Intro to Probability]] || William
|| HW1 Probabilities
|| Will, Han
|-
| M 1/18 ||colspan="4"| ''Martin Luther King Day'' 
|- 
| W 1/20 || [[10-601 Naive Bayes|The Naive Bayes algorithm]] || William 
|| HW2: implementing naive Bayes || Travis, Maria
|- 
| M 1/25 ||  [[10-601 Logistic Regression|Logistic Regression]] || William ||  || 
|- 
| W 1/27 ||  [[10-601 Linear Regression|Linear Regression]] || William  || || 
|-
| M 2/1 || [[10-601 Perceptrons and Voted Perceptrons|Perceptrons]] and [[10-601 SVMS|SVMs]]  || Nina ||  || 
|-
| W 2/3 || Kernels  || Nina || HW3: implementing logistic regression and linear regression || Tianshu, Will  
|- 
| M 2/8 || [[10-601 Neural_networks_and_Deep_Belief_Networks|Neural Networks and Backprop]] || Nina || ||
|- 
| W 2/10 || [[10-601 Decision Trees|Decision Trees and Rules]] || Nina || ||
|-
| M 2/15 || [[10-601 Ensembles|Boosting and Other Ensembles]] || Nina || ||
|- 
| W 2/17 || [[10-601 PAC| Theory 1]] || Nina ||  HW4: Theory || Han, Tianshu
|- 
| M 2/22 || Theory 2|| Nina || ||
|- 
| W 2/24|| Midterm Review || Nina || ||
|- 
| M 2/29 ||colspan="4"| ''Midterm exam'' 
|- 
| W 3/2 || [[10-601 Clustering| Unsupervised Learning: k-Means and Mixtures]] || Nina || ||
|- 	                                                                                    
| M 3/7	|| [[10-601 SSL| Semi-Supervised Learning]] || Nina || || 
|- 
| W 3/9	|| Active Learning || Nina || HW5: Active learning and clustering || Travis, Han
|-
| M 3/14 ||colspan="4"| ''Spring break'' 
|-
| W 3/16 ||colspan="4"|  ''Spring break''
|- 
| M 3/21 || [[10-601 GM1| Graphical Models 1]]  || William || ||  		    
|- 	                                                                                    
| W 3/23 || [[10-601 GM2| Graphical Models 2]] ||  William  || || 
|-
| M 3/28 || [[10-601 Sequences|Graphical Models for Sequential Data]] || William ||HW6: Graphical models || Maria, Renato
|-
| M 4/4 || [[10-601 Topic Models|Topic Models]] || William || ||
|-
| W 4/6 || Deep Learning 1 || William || || 
|-
| M 4/11 || Deep Learning 2 || William || ||
|-
| W 4/13 ||  [[10-601_Matrix_Factorization|PCA and dimension reduction]] || William || HW7: Deep Learning || TBA
|-
| M 4/18 || [[10-601_Matrix_Factorization|Matrix Factorization and collaborative filtering]] || William || ||
|-
| W 4/20 || Reinforcement Learning || Nina || ||
|-
| M 4/25 || Review || Nina and William || ||
|-
| W 4/27 || ''Final exam'' ||  || ||
|}

'''To other instructors''': if you'd like to use any of the materials found here, you're absolutely welcome to do so, but please acknowledge their ultimate source somewhere.

== Other Lectures ==

* [[10-601 Wrap-up on Linear Classification]]