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