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Introduction to Machine Learning (10-701)

Fall 2017

Barnabás Póczos, Ziv Bar-Joseph

School of Computer Science, Carnegie Mellon University

Syllabus and (tentative) Course Schedule

This schedule is tentative and subject to change. Please check back often.
Reading listed for each lecture is not mandatory unless otherwise specified

Legend:

  • TM : Machine Learning, Tom Mitchell
  • KM : Machine Learning: a Probabilistic Perspective, Kevin Murphy
  • CB : Pattern Recognition and Machine Learning, Chris Bishop
  • DM : Information Theory, Inference, and Learning Algorithms, David Mackay

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