| Week |
Topic(s) |
Slide(s) |
Additional Resources |
Week 0I
02.10 |
Course
Information and Introduction
Chapter 1 - Introduction
|
Lec 1:
pdf |
-
http://aima.cs.berkeley.edu/
-
Berkeley AI
Materials
-
CS 188
-
CS 188 Archives
-
Fall
2018 - CS 188
-
Spr
2024 - CS 188
- Stanford AI - CS 221
-
CS 221 autumn modules
-
Video : CS 188 Spr 2024 Lec 01
- Video: Pieter
Abbeel giving the introductory lecture for the Spring 2014
Berkeley CS 188 course
- Video: Dan
Klein giving the introductory lecture for the Fall 2013
Berkeley CS 188 course
|
Week 02
09.10 |
Chapter 2 - Intelligent Agents
|
Lec 2 :
pdf |
-
https://inst.eecs.berkeley.edu/~cs188/fa23/assets/notes/cs188-fa23-note01.pdf |
Week 03
16.10 |
Chapter 3 - State Spaces, Problem Solving by uninformed Search |
Lec 3:
pdf,
pptx |
- Video:
CS 188 Spr 2024 Lec2
CS 188 Spr 2024 Lec2
- Video: Dan
Klein giving the informed search lecture for the Fall 2012
Berkeley CS 188 course
- Video: Pieter
Abbeel giving the informed search lecture for the Fall 2013
Berkeley CS 188 course
- Video:
Fall
2018 CS 188 |
Week 04
23.10 |
Chapter 3 - Problem Solving by informed Search |
Lec 4: pdf,
pptx |
- Video:
CS 188 Spr 2024 Lec3
- Video:
CS 188 Spr 2024 Lec4
- Video: Dan
Klein giving the informed search lecture for the Fall 2012
Berkeley CS 188 course
- Video: Pieter
Abbeel giving the informed search lecture for the Fall 2013
Berkeley CS 188 course
- Video:
Fall
2018 CS 188 |
Week 06
03.11 |
Chapter 4 - Search in Complex Environments |
Lec 05: pdf,
pptx |
- Video:
CS Spr
2024 Local Searches |
Week 07
06.11 |
Chapter 5 - Constraint
Satisfaction Problems |
Lec 06: pdf,
pptx |
- Video:
CS 188
Fall 2025 CSP I
- Video:
CS 188
Fall 2025 CSP II
- Video: Pieter
Abbeel giving the first constraint satisfaction problem
lecture for the Spring 2013 Berkeley CS 188 course
-
Video: Pieter
Abbeel giving the second constraint satisfaction problem for
the Spring 2014 Berkeley CS 188 course
|
| |
Midterm Week |
|
|
Week 08
20.11 |
Chapter 6 - Adversarial
Search and Games |
Lec 7:
pdf,
pptx |
- Video: Pieter
Abbeel giving the adversarial search lecture for the Spring
2014 Berkeley CS 188 course
- Video: Pieter
Abbeel giving the expectimax lecture for the Spring 2014
Berkeley CS 188 course |
Week 09
27.11 |
Chapter 16 - Markov
Decision Process |
Lec 8:
pdf,
pptx |
- Video: Pieter
Abbeel giving the MDPs I lecture for the Spring 2014
Berkeley CS 188 course
- Video: Pieter
Abbeel giving the MDPs II lecture for the Spring 2014
Berkeley CS 188 course
-
https://inst.eecs.berkeley.edu/~cs188/fa23/assets/lectures/cs188-fa23-lec08.pdf |
Week 10
04.12 |
Chapter 23 - Reinforcement
Learning |
Lec 9:
pdf,
pptx |
- Video: Pieter
Abbeel giving the reinforcement learning I lecture for the
Spring 2014 Berkeley CS 188 course
- Video: Pieter
Abbeel giving the reinforcement learning II lecture for the
Spring 2014 Berkeley CS 188 course
|
Week 11
11.12 |
Chapter 19,20,21 - Machine
Learning
ML I : Naive Bayes |
Lec 10:
pdf,
pptx
|
-
https://inst.eecs.berkeley.edu/~cs188/fa23/
-
https://inst.eecs.berkeley.edu/~cs188/fa18/
-
https://stanford-cs221.github.io/autumn2023/
-
https://stanford-cs221.github.io/spring2023/
-
PythonDataScienceHandbook - ML
-
DM
Slides
CS 221 ML 1 : Linear regression and Linear
classification - Local Copy (PDF,
code) |
Week 12
18.12 |
Chapter 19,20,21 - Machine
Learning
ML II : Perceptrons and
Logistic Regression |
Lec 11:
pdf,
pptx
|
-
https://inst.eecs.berkeley.edu/~cs188/fa23/
-
https://inst.eecs.berkeley.edu/~cs188/fa18/
-
https://stanford-cs221.github.io/autumn2023/
-
https://stanford-cs221.github.io/spring2023/
-
PythonDataScienceHandbook - ML
CS 221 ML 2
: SGD, feature templates, non-linear features, neural
networks - Local Copy (PDF,
code) |
Week 13
25.12 |
Chapter 19,20,21 - Machine
Learning
ML III : Optimization and Neural
Networks
Neural Networks II and Decision Trees
Presentations |
Lec 12 :
-
pdf1,
pptx1
-
pdf2,
pptx2 |
-
https://inst.eecs.berkeley.edu/~cs188/fa23/
-
https://inst.eecs.berkeley.edu/~cs188/fa18/
-
https://stanford-cs221.github.io/autumn2023/
-
https://stanford-cs221.github.io/spring2023/
-
PythonDataScienceHandbook - ML
CS 221 ML 3 :
Backpropagation, K-means, generalization, and best practices
- Local Copy (PDF)
- K-means demo from CS221 (html) |
Week 14
02.01 |
Chapter 22 - Deep
Learning
Presentations |
Lec 13 |
- CS 221 DL - Local Copy (pdf)
- Deep Learning Overview
(PPTX)
- Dive into Deep Learning (html)
- https://huggingface.co/docs/transformers/index
-
https://jalammar.github.io/illustrated-transformer/
-
https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software
-
https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learning-frameworks |
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