This schedule is tentative and subject to change. Please check back often.
Reading listed for each lecture is not mandatory unless otherwise specified
Legend:
Date | Lecture | Topics | Relevant Reading | Announcements |
---|---|---|---|---|
Mon 28-Aug |
1: Introduction [Slides] |
Introductory Math MLE |
TM : Chapters 1, 2 | |
Wed 30-Aug |
2: Classification [Slides] [Class_Notes] |
KNN | TM : Chapter 8 | |
Mon 4-Sept |
LABOR DAY HOLIDAY |
|||
Wed 6-Sept |
3: Naive Bayes [Slides] |
TM : 6.1 - 6.10 | HW1 out | |
Mon 11-Sept |
4: Decision Trees [Slides] |
TM : Chapter 3 | ||
Wed 13-Sept |
5: Linear Regression [Slides] |
TM : Chapter 4.1-4.3 | ||
Mon 18-Sept |
6: Logistic Regression [Slides] |
KM : 8.1 - 8.3, 8.6 | ||
Wed 20-Sept |
7: Perceptron [Slides] |
[10601_Notes] [Barnabas'_Notes] | HW1 due HW2 out |
|
Mon 25-Sept |
8: Neural Networks 1 [Slides] [Matlab_Demos] |
[Online_Book] TM : Chapter 4 CB : Chapter 5 |
||
Wed 27-Sept |
9: Neural Networks 2: Deep Learning [Slides] |
[CNN_Notes]
[MLP_Notes] [Perceptron_Notes] [ImageNet_Paper] [Bengio_Deep_Learning] |
||
Mon 2-Oct |
10: Applications of Neural Networks [Slides] |
|||
Wed 4-Oct |
11: Support Vector Machines - 1 [Slides] |
KM: Chapter 14 CB: Chapters 6 & 7 |
HW2 due HW3 out |
|
Mon 9-Oct |
12: Support Vector Machines - 2 [Slides] |
[SVM_Projected_Notes]
KM: Chapter 14 CB: Chapters 6 & 7 |
||
Wed 11-Oct |
13: Ensemble Learning and Boosting [Slides] |
[Boosting_Projected_Notes]
CB: 14.3 |
||
Mon 16-Oct |
14: Active Learning [Slides] |
[Settles_Notes]
[Balcan_Notes] [Krause_2008] |
||
Wed 18-Oct |
15: Unsupervised Learning (Clustering) - 1 [Slides] |
[EM_Notes]
[MoG_Notes] |
HW3 due | |
Mon 23-Oct |
16: Clustering - 2 [Slides] |
|||
Wed 25-Oct |
MIDTERM 5:00 PM (Location: MM 103 and MM A14) [Google Maps] |
HW4 out | ||
Mon 30-Oct |
17: Dimensionality Reduction - 1 (PCA) [Slides] |
[PCA_reading] | ||
Wed 1-Nov |
18: Dimensionality Reduction - 2 (ICA) [Slides] |
[ICA_reading] | ||
Mon 6-Nov |
19: Semi-supervised Learning [Slides] |
[SSL_Survey] | ||
Wed 8-Nov |
20: Learning Theory - 1 [Slides] |
[Learning_Theory_Notes (p1-19)] | HW4 due | |
Mon 13-Nov |
21: Learning Theory - 2 [Slides] |
|||
Wed 15-Nov |
22: Graphical Models (Bayesian Networks)- 1 [Slides] |
HW5 out | ||
Mon 20-Nov |
23: Graphical Models (Markov Random Fields) - 2 [Slides] [Lecture Video] |
|||
Wed 22-Nov |
THANKSGIVING | |||
Mon 27-Nov |
24: Guest Lecture: Zoltán Szabó (CMAP, École Polytechnique) [Slides] |
|||
Wed 29-Nov |
25: Hidden Markov Models - 1 [Slides] |
[Jordan_GM_Notes] | ||
Mon 4-Dec |
26: Hidden Markov Models - 2 [Slides] [Additional_Slides] |
HW5 due | ||
Wed 6-Dec |
27: Large-Scale Question Answering on Knowledge Bases and Text |