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UMBC CMSC 671 Fall 2020 |
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![]() Schedule
|
| # | Day | Date | Topic | Read 1st | Slides |
Video | Quiz, Homework |
Seealso |
|---|---|---|---|---|---|---|---|---|
| 1 | Thr | 8/27 | Administrivia, Introduction, history | RN1 | 00 01 |
L01 | hw1 out | History of AI |
| 2 | Tue | 9/1 | Agents | RN2 |
L02 | Software agents | ||
| 3 | Thr | 9/3 | Problem solving as search | RN3 |
L03 |
|
||
| 4 | Tue | 9/8 | Uninformed & Informed search | RN4 |
L04 |
|
Missionaries and cannibals, Water jugs, aima code, wj.py, wj.ipynb |
|
| 5 | Thr | 9/10 | Informed search | RN4 |
L05 | |||
| 6 | Tue | 9/15 | Informed Search |
RN4 |
L06 | |||
| 7 | Thr | 9/17 | Constraints | RN5 |
L07 |
|
CCC site, 8 queens CSP, csp.py, CSP
demo, SLS
CSP demo, Genetic algorithm |
|
| 8 | Tue | 9/22 | Constraints |
RN5 |
L08 |
|
ms3.py, mapc.py, sudoku.py, python-constraints | |
| 9 | Thr | 9/24 | Games |
RN 6 |
L09 | Checkers solved; U. Alberta Games Group; AlphaGo |
||
| 10 | Tue | 9/29 | Games | RN 6 |
L10 | It, New Yorker, 1952; | ||
| 11 | Thr | 10/1 | Games Theory | RN 17.6 |
L11 | game theory, PD demo, PD lessons, Prisoner's Dilema, Chicken, Evolution of Trust |
||
| 12 | Tue | 10/6 | Reasoning Agents | RN7 |
L12 | Hunt the Wumpus; neats vs scruffies | ||
| 13 | Thr | 10/8 | Reasoning Agents | RN7 |
L13 | Knowledge Base; Wason selection task; | ||
| 14 | Tue | 10/13 | MIDTERM EXAM |
material thru 10/1, 75 minute exam held online between 1 and 3 |
||||
| 15 | Thr | 10/15 | KR and FOL | RN8 |
L14 | see notes 9.3.1 and 9.3.2 | ||
| 16 | Tue | 10/20 | FOL | RN9 | 09 | L15 | see notes 9.3.2 and 9.4.1 | |
| 17 | Thr | 10/22 | Knowledge representation | RN10 |
L16 | hw4 out | ||
| 18 | Tue | 10/24 |
Planning |
RN11 |
13 |
L17 | STRIPS, Planning and scheduling | |
| 19 | Thr | 10/29 | Planning & Bayes | RN11, RN12 |
13, 15 | L18 | ||
| 20 | Tue | 11/3 | no class | -- |
-- |
-- | -- | -- |
| 21 | Thr | 11/5 | Bayesian Networks | RN13 |
15 |
L19 | hw4 due |
Netica BBN Tutorial |
| 22 | Tue | 11/10 | Machine Learning | RN19 video |
14 | L20 |
|
Google's Rules of Machine Learning, Unreasonable Effectiveness of Data |
| 23 | Thr | 11/12 | Decision trees | RN19 |
L21 | |||
| 24 | Tue | 11/17 | Decision trees, SVMs, tools | RN19 video |
L22 | hw5 due | Support vector machine |
|
| 25 | Thr | 11/19 | methodology, clustering | RN19 |
L23 | hw6 out | Training, validation, test sets, Precision and recall, FI, Cluster analysis, colab notebooks |
|
| 26 | Tue | 11/24 | hierarchical clustering, bagging | RN19 |
L24 | hierarchical clustering, ensemble learning, colab clustering notebooks |
||
| -- | Thr | 11/26 | -- | -- |
--
|
-- | -- | |
| 27 | Tue | 12/1 | neural networks | RN21 |
14_01 | L25 | colab notebooks, NN playground | |
| 28 | Thr | 12/2 | neural networks | RN21 | 14_02 | L26 | hw6 due | colab notebooks, keras.io |
| 29 | Tue | 12/8 | deep learning, wrap up | RN21 | 14_03 | L27 | word2vec demo | |
| -- | Wed | 12/9 | -- | -- | -- | |||
| -- | Tue |
12/15, 1-3pm |
FINAL EXAM | see above |
see above |
old exams |