Lectures
Rocco's lecture notes will be posted soon after the class. You can also find course videos in the "Video Library" section of Courseworks soon after class.
Warning: the notes below were generated in real time and have not been edited. They may contain typos or other errors. They may also contain aesthetically displeasing color combinations.
| Number |
Date |
Topics |
Notes |
References |
| 1 |
Tues Sept 6 |
Introduction, basics |
|
|
| 2 |
Thurs Sept 8 |
|
|
|
Schedule of Topics
Here is an anticipated list of topics. Note that the ordering
of some topics may change, and we may spend more or less than one lecture per
topic.
- Introduction to machine learning theory. Learning models and learning problems.
- Online mistake bound learning. Algorithms for simple concept classes. Attribute efficient learning with the Winnow algorithm.
- Winnow algorithm continued. Perceptron algorithm.
General bounds for online mistake bound learning. The Halving algorithm, the Standard Optimal Algorithm.
- Best Experts and Weighted Majority.
- The Probably Approximately Correct(PAC) Learning model. PAC learning algorithms for simple concept classes.
- More PAC learning algorithms. Conversions from online
learning to PAC learning. (KV chapter 1)
- Occam's Razor: learning by finding consistent hypotheses.
Applications (KV chapter 2).
- Computational hardness results for finding consistent
hypotheses.
- Vapnik-Chervonenkis dimension. Upper and lower
bounds on sample complexity (KV chapter 3).
- VC dimension and sample complexity continued.
- VC dimension and sample complexity continued.
- Weak learning, strong learning, and Boosting (KV
chapter 4).
- Boosting continued.
- Boosting continued.
- Learning in the presence of noise. Classification
noise, malicious noice,
- Statistical Query learning (KV chapter 5).
- Learning with noise continued.
- Learning with noise continued.
- Cryptographic limitations on efficient learning (KV
chapter 6).
- Cryptographic limitations on learning continued.
- Cryptographic limitations and reductions in
PAC learning (KV chapters 6,7).
- The model of exact learning from membership and
equivalence queries. Learning monotone DNF formulas.
- Learning deterministic finite automata.
- Learning deterministic finite automata continued.