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 |

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.