Machine Learning
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Google Group
Lectures
1 Introduction
2 Basic Statistics
3 Instance based methods
4 Perceptron
5 Support Vectors
6 Kernels
7 Kernels
8 Stochastic Convergence
9 Tail Bounds
10 Risk Minimization
11 Learning Theory
12 Gaussian Processes
13 Exponential Families
14 PCA
15 Graphical Models
16 Dynamic Programming
17 Latent Variables I
18 Latent Variables II
19 EM and Clustering
20 ICA
21 Sampling
21 Trees
22 Neural Networks
23 Boosting
24 Reinforcement Learning
25 Scalability
Projects
Recitations
1 Statistics
2 Linear Algebra
3 Optimization
4 Model Selection
5 Convergence Bounds
6 Kernels
7 Risk. Gaussian Processes
8 Exponential Families
9 Information Theory
CMU
Alex Smola
Barnabas Poczos
Ina Fiterau
Junier Oliva
Leila Wehbe
Mu Li
Xuezhi Wang
MLD
CMU

Contact

Introduction to Machine Learning - 10-701/15-781

Overview

If you have questions, you should do the following:

  • If it's a question that other students also might have, ask it on the Google Group first.

  • If this doesn't solve your question or if it's not something you'd like to discuss in public, send an e-mail with 10-701 in the subject.

  • Come to the office hours. By default they're open for all. And you're welcome to stick around while we're answering other students’ questions.

  • Alex has office hours on Tuesday 2-4pm in Gates 8002. Barnabas’ office hours are Thursday 10am-12pm in Gates 8231.

Decisions

Both Barnabas and Alex decide (if you ask both the same question you'll get the intersection of the decisions, i.e. you're guaranteed never to be better off by trying to game the system). Their decisions are final. With regard to the assignments, the TAs decide. Their decisions are final (i.e. asking Alex and Barnabas to override a TA grading decision is not an effective use of your time).