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UMBC CMSC 471 Spring 2022
Introduction to Artificial Intelligence

Schedule

subject to change

# Day Date Topic Read first Slides
Lecture Video Quiz,
Homework
Seealso
1 Tue 2/1 Administrivia, Introduction, history RN1 00
01
L01 hw1 out History of AI
2 Thr 2/3 Agents
RN2

02

L02   Software agents
3 Tue 2/8 Problem solving as search
RN3
L03


Graph traversal algorithms, PHW on Search

4 Thr 2/10 Uninformed search
RN4
L04

hw1 due
hw2 out

Missionaries and cannibals, Water jugs, aima code, wj.py, wj.ipynb

5 Tue 2/15 Informed search
RN4 
L05

Search demo, A* algorithm, 8 puzzle visualization, p8.py, 8 Queens problem,

6 Thr 2/17 Informed Search
RN4
L06


Hill Climbing, Simulated annealing, Genetic algorithm, Tabu Search

7 Tue 2/22 Constraints
RN5
L07


CCC site, 8 queens CSP, csp.py, CSP demo, SLS CSP demo

8 Thr 9/24

Constraints

RN5
L08

hw2 by 2/26

ms3.py, mapc.py, sudoku.py, python-constraints
9 Tue 3/1 Games
RN 6
L09
hw3 out
Checkers solved; U. Alberta Games Group; AlphaGo
10 Thr 3/3 Games
RN 6
L10
It, New Yorker, 1952;
11 Tue 3/8 Game Theory

RN 18.2

L11

game theory, PD demo, PD lessons, Prisoner's Dilemma, Chicken, Evolution of Trust

12 Thr 3/10 Reasoning Agents
RN7
L12 hw3 due 3/28 Hunt the Wumpus; neats vs scruffies, Wason selection task;
13 Tue 3/15 Reasoning Agents
RN7
L13   Knowledge Base;
14 Thr

3/17

MIDTERM EXAM

RN 1-6; 17.6
01-09
--  

material thru lecture 11 (3/8), 75 minute exam held in class

-- Tue 3/22 BREAK -- -- -- --
-- Thr 3/24 BREAK -- -- -- --
15 Tue 3/29 KR, FOL
RN 8,9
L14
hw3 due 3/28
tue_3/29, , see notes 9.3.1 and 9.3.2
16 Thr 3/31 KR, FOL RN8, 9 09 L15 HW4 out see notes 9.3.2 and 9.4.1; family.pl, genesis.pl
17 Tue 4/5 FOL, Planning
RN10
L16   see notes 13, STRIPS, Planning and scheduling,
18 Thr 4/7

Planning

RN11
L17   PDDL, planning.domains, planning repo
19 Tue 4/12 Probability & Bayesian Reasoning
RN12
15.1 L18
HW4 due 4/13

Bayes theorem video

20 Thr 4/14 BBNs
RN 13
L19   Netica BBN Tutorial, Colab notebook
21 Tue 4/19 Machine learning, decision trees
RN19
L20


Google's Rules of Machine Learning, Unreasonable Effectiveness of Data
22 Thr 4/21 Decision trees, ML tools RN19
video
14 L21

 

Decision tree learning, weka, scikit-learn
23 Tue 4/26 ML tools, Methdology

RN19

L22  

Training, validation, test sets, Precision and recall, FI

24 Thr 4/28 SVMs, clustering
RN19
video
L23 HW5 out Support vector machine, Cluster analysis, colab notebooks
25 Tue 5/3 clustering
RN19
L24   hierarchical clustering
26 Thr 5/5 clustering, bagging, neural networks
RN19
L25   ensemble learning, colab clustering notebooks, Bootstrap aggregating, artificial neural networks
27 Tue 5/10 neural networks
RN21
L26   colab notebooks, NN playground, backpropagation
28 Thr 5/12 neural networks RN21
18_NLP L27 HW5 due colab notebooks, keras.io, word2vec demo
29 Tue 5/17 word embeddings, Transformers RN21 14_03, 14_04 L28   notebook, Huggingface transformers; OpenAI
-- tbd

5/24

Final Tue
3:30-5:30
see
above
see
above
see
above

 

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