ai-learning-roadmap

Lists of all AI related learning materials and practical tools to get started with AI apps

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Design Thinking – An Introduction

• Stanford's virtual Crash Course in Design Thinking

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Amazon Web Services Learning Material

• AWS AI Session– The session provides an overview of all Amazon AI technology offerings (Lex, Polly, Rekognition, ML, and Deep Learning AMI)

Self-Paced Labs

AWS self-paced labs provide hands-on practice in a live AWS environment with AWS services and real-world cloud scenarios. Follow step-by-step instructions to learn a service, practice a use case, or prepare for AWS Certification.

Introductory Lab

• Introduction to AWS Lambda

Lex

• Introduction to Amazon Lex
• Amazon Lex Webinar
• Amazon Lex: AWS conversational interface (chat bot)
• Documentation

Polly

• Introduction to Amazon Polly
• Amazon Polly Webinar -
• Amazon Polly – AWS Text To Speech (TTS) service
• Documentation
  • What is Amazon Polly?
  • Developer Resources

Rekognition

• Introduction to Amazon Rekognition
• Amazon Rekognition - Deep Learning-Based Image Analysis Webinar
• Amazon Rekognition – AWS image recognition service
• Documentation – What is Amazon Rekognition?

Machine Learning

• Machine Learning

• Session 1 – Empowering Developers to Build Smart Applications

• Session 2 - Predicting Customer Churn with Amazon Machine Learning

• AWS Machine Learning – End to end, managed service for creating and testing ML models and then deploying those models into production

• Documentation

  • What is Amazon Machine Learning?
  • Developer Resources

• AWS Deep Learning AMI – Amazon Machine Image (AMI) optimized for deep learning efforts

Recommended Additional Resources

Take your skills to the next level with fundamental, advanced, and expert level labs.

• Creating Amazon EC2 Instances with Microsoft Windows
• Building Your First Amazon Virtual Private Cloud (VPC)
• Working with AWS CodeCommit on Windows
• Working with Amazon DynamoDB

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Google Cloud - Learning Material

Below is the learning material that will help you learn about Google Cloud.

Network

• Networking 101 – 43 mins

The codelab provides common cloud developer experience as follows:

• Set up your lab environment and learn how to work with your GCP environment.
• Use of common open source tools to explore your network around the world.
• Deploy a common use case: use of HTTP Load Balancing and Managed Instance Groups to host a scalable, multi-region web server.
• Testing and monitoring your network and instances.
• Cleanup.

Developing Solutions for Google Cloud Platform – 8 hours

Infrastructure

• Build a Slack Bot with Node.js on Kubernotes – 43 mins
• Creating a Virtual Machine – 10 mins
• Getting Started with App Engine (Python) – 13 mins

Data

• Introduction to Google Cloud Data Prep – 7 mins
• Create a Managed MySQL database with Cloud SQL – 19 mins
• Upload Objects to Cloud Storage – 11 mins

AI, Big Data & Machine Learning

• Introduction to Google Cloud Machine Learning – 1 hour
• Machine Learning APIs by Example – 30 min
• Google Cloud Platform Big Data and Machine Learning Fundamentals

Additional AI Materials

• Auto-awesome: Advanced Data Science on Google Cloud Platform – 45 min
• Run a Big Data Text Processing Pipeline in Cloud Dataflow – 21 min
• Image Classification Using Cloud ML Engine & Datalab – 58 min
• Structured Data Regression Using Cloud ML Engine & Datalab – 58 min

(Optional) Deep Learning & Tensorflow

• Tensorflow and Deep Learning Tutorial – 2:35 hours
• Deep Learning Course – advanced users only

Additional Reference Material

• Big Data & Machine Learning @ Google Cloud Next '17 - A collection of 49 videos

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IBM Watson Learning Material

(Contributions are welcome in this space)

• IBM Watson Overview
• IBM Watson Cognitive APIs
• IBM Watson Knowledge Studio

Visual Studio

UCI datasets

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Microsoft Chat Bots Learning Material

Skills Prerequisite

• Git and Github
• NodeJS
• VS Code IDE

Training Paths

If you have the above Prerequisite skills, then take Advanced Training Path else take Novice Training Path.

Prerequisite Tutorials

• Git and Github
• Node.js
  • Node.js Tutorials for Beginners
  • Node.js Tutorial in VS Code
• Introduction To Visual Studio Code

Novice Training Path

Environment Set Up

• Download and Install Git
• Set up GitHub Account_
• Download and Install NodeJS
• Download and Install IDE - Visual Studio Code
• Download and Install the Bot Framework Emulator
• Git clone the Bot Education project - git clone
• Set Up Azure Free Trial Account

Cognitive Services (Defining Intelligence)

• Read Cognitive Services ADS Education Deck – git clone
• Review the guide for Understanding Natural language with LUIS
• Complete the NLP (LUIS) Training Lab from the installed Bot Education project – \bot-education\Student-Resources\Labs\CognitiveServices\Lab_SetupLanguageModel.md

Bot Framework (Building Chat Bots)

• Read Bot Framework ADS Education Deck from downloaded - (Your Path)\bot-extras
• Review Bot Framework documentation (Core Concepts, Bot Builder for NodeJS, and Bot Intelligence) -
• Setup local environment and run emulator from the installed Bot Education project – \bot-education\Student-Resources\Labs\Node\Lab1_SetupCheckModel.md
• Review and test in the emulator the “bot-hello” from \bot-education\Student-Resources\BOTs\Node\bot-hello

Advanced Training Path

Environment Set Up

• Download and Install Git
• Set up GitHub Account_
• Download and Install NodeJS
• Download and Install IDE - Visual Studio Code
• Download and Install the Bot Framework Emulator
• Git clone the Bot Education project - git clone
• Set Up Azure Free Trial Account
• Git clone the Bot Builder Samples – git clone

Cognitive Services (Defining Intelligence)

• Read Cognitive Services ADS Education Deck – git clone
• Review the guide for Understanding Natural language with LUIS

Bot Framework (Building Chat Bots)

• Read Bot Framework ADS Education Deck from downloaded - (Your Path)\bot-extras
• Review Bot Framework documentation (Core Concepts, Bot Builder for NodeJS, and Bot Intelligence) -
• Setup local environment and run emulator from the installed Bot Education project – \bot-education\Student-Resources\Labs\Node\Lab1_SetupCheckModel.md

Cognitive Services (Defining Intelligence) - Labs

• Complete the NLP (LUIS) Training Lab from the installed BOT Education project
  • \bot-education\Student-Resources\Labs\CognitiveServices\Lab_SetupLanguageModel.md
• Review, Deploy and run the LUIS BOT sample

Bot Framework (Building Chat Bots) – Labs

• Setup local environment and run emulator from the installed Bot Education project
  • \bot-education\Student-Resources\Labs\Node\Lab1_SetupCheckModel.md
• Review and test in the emulator the “bot-hello” from
  • \bot-education\Student-Resources\BOTs\Node\bot-hello
• Review and test in the emulator the “bot-recognizers” from
  • \bot-education\Student-Resources\BOTs\Node\bot-recognizers

---

Lecture Videos

Source Berkeley

    **Lecture Title****Lecturer**Semester
    
      Lecture 1
      [Introduction](https://www.youtube.com/watch?feature=player_embedded&v=J6PBD-wNEDs)
      Dan Klein
      Fall 2012

      Lecture 2
      [Uninformed Search](https://www.youtube.com/watch?feature=player_embedded&v=afwPe_OqPX0)
      Dan Klein
      Fall 2012

      Lecture 3
      [Informed Search](https://www.youtube.com/watch?feature=player_embedded&v=OGcG4jSKOVA)
      Dan Klein
      Fall 2012

      Lecture 4
      [Constraint Satisfaction Problems I](https://www.youtube.com/watch?feature=player_embedded&v=hegL0V4ckco)
      Dan Klein
      Fall 2012

      Lecture 5
      [Constraint Satisfaction Problems II](https://www.youtube.com/watch?feature=player_embedded&v=LajAWn51HmE)
      Dan Klein
      Fall 2012

      Lecture 6
      [Adversarial Search](https://www.youtube.com/watch?feature=player_embedded&v=cwbjLIahbv8)
      Dan Klein
      Fall 2012

      Lecture 7
      [Expectimax and Utilities](https://www.youtube.com/watch?feature=player_embedded&v=GevK0-9n24g)
      Dan Klein
      Fall 2012

      Lecture 8
      [Markov Decision Processes I](https://www.youtube.com/watch?feature=player_embedded&v=wKx4MuLfe0M)
      Dan Klein
      Fall 2012

      Lecture 9
      [Markov Decision Processes II](https://www.youtube.com/watch?feature=player_embedded&v=2M7mv4-BPCg)
      Dan Klein
      Fall 2012

      Lecture 10
      [Reinforcement Learning I](https://www.youtube.com/watch?feature=player_embedded&v=hsz0zq6AXGE)
      Dan Klein
      Fall 2012

      Lecture 11
      [Reinforcement Learning II](https://www.youtube.com/watch?feature=player_embedded&v=R0vTZp0ve4s)
      Dan Klein
      Fall 2012

      Lecture 12
      [Probability](https://www.youtube.com/watch?v=cFtXkaLog5A)
      Pieter Abbeel
      Spring 2014

      Lecture 13
      [Markov Models](https://www.youtube.com/watch?v=Nxkapm7QlNw)
      Pieter Abbeel
      Spring 2014

      Lecture 14
      [Hidden Markov Models](https://www.youtube.com/watch?v=OdQoRGRPmj8)
      Dan Klein
      Fall 2013

      Lecture 15
      [Applications of HMMs / Speech](https://www.youtube.com/watch?v=KBg97801U40)
      Pieter Abbeel
      Spring 2014

      Lecture 16
      [Bayes' Nets: Representation](https://www.youtube.com/watch?v=gMQZq2O8yDA)
      Pieter Abbeel
      Spring 2014

      Lecture 17
      [Bayes' Nets: Independence](https://www.youtube.com/watch?v=9OajWBYRhRU)
      Pieter Abbeel
      Spring 2014

      Lecture 18
      [Bayes' Nets: Inference](https://www.youtube.com/watch?v=y9jdsPNsU_Q)
      Pieter Abbeel
      Spring 2014

      Lecture 19
      [Bayes' Nets: Sampling](https://www.youtube.com/watch?v=ogYaTTRguIw)
      Pieter Abbeel
      Fall 2013

      Lecture 20
      [Decision Diagrams / Value of Perfect Information](https://www.youtube.com/watch?v=VL_IAzfC2uk)
      Pieter Abbeel
      Spring 2014

      Lecture 21
      [Machine Learning: Naive Bayes](https://www.youtube.com/watch?v=_pe60buCnxE)
      Nicholas Hay
      Spring 2014

      Lecture 22
      [Machine Learning: Perceptrons](https://www.youtube.com/watch?v=HEFfs1KCph4)
      Pieter Abbeel
      Spring 2014

      Lecture 23
      [Machine Learning: Kernels and Clustering](https://www.youtube.com/watch?v=H9DUTH9lCfg)
      Pieter Abbeel
      Spring 2014

      Lecture 24
      [Advanced Applications: NLP, Games, and Robotic Cars](https://www.youtube.com/watch?v=sMKptCC-x_o)
      Pieter Abbeel
      Spring 2014

      Lecture 25
      [Advanced Applications: Computer Vision and Robotics](https://www.youtube.com/watch?v=WZqtZ8RDVs8)
      Pieter Abbeel
      Spring 2014

Additionally, there are additional Step-By-Step videos which supplement the lecture's materials. These videos are listed below:

    **Lecture Title****Lecturer**Notes
    
      SBS-1
      [DFS and BFS](https://www.youtube.com/watch?v=cXZKV7K5v3E)
      Pieter Abbeel
      Lec: Uninformed Search

      SBS-2
      [A* Search](https://www.youtube.com/watch?v=g0MJRpquEOk)
      Pieter Abbeel
      Lec: Informed Search

      SBS-3
      [Alpha-Beta Pruning](https://www.youtube.com/watch?v=jvpWtwVSvjA)
      Pieter Abbeel
      Lec: Adversarial Search

      SBS-4
      [D-Separation](https://www.youtube.com/watch?v=_R_RYn5KelA)
      Pieter Abbeel
      Lec: Bayes' Nets: Independence

      SBS-5
      [Elimination of One Variable](https://www.youtube.com/watch?v=-Y-XiO9VeLQ)
      Pieter Abbeel
      Lec: Bayes' Nets: Inference

      SBS-6
      [Variable Elimination](https://www.youtube.com/watch?v=4-Cr_Lv9Sr4)
      Pieter Abbeel
      Lec: Bayes' Nets: Inference

      SBS-7
      [Sampling](https://www.youtube.com/watch?v=Q6NzkeUvj2I)
      Pieter Abbeel
      Lec: Bayes' Nets: Sampling

SBS-8 Gibbs' Sampling Michael Liang Lec: Bayes' Nets: Sampling -->

      SBS-8
      [Maximum Likelihood](https://www.youtube.com/watch?v=b2g88r69vPI)
      Pieter Abbeel
      Lec: Machine Learning: Naive Bayes

      SBS-9
      [Laplace Smoothing](https://www.youtube.com/watch?v=VkBytl_Qmt8)
      Pieter Abbeel
      Lec: Machine Learning: Naive Bayes

      SBS-10
      [Perceptrons](https://www.youtube.com/watch?v=mH4QSs5IGao)
      Pieter Abbeel
      Lec: Machine Learning: Perceptrons

******************

Per-Semester Video Archive(Berkeley)

The lecture videos from the most recent offerings are posted below.

  [Spring 2014 Lecture Videos](#Spring2014)

  [Fall 2013 Lecture Videos](#Fall2013)

  [Spring 2013 Lecture Videos](#Spring2013)

  [Fall 2012 Lecture Videos](#Fall2012)

Spring 2014

    **Lecture Title****Lecturer**Notes
    
      Lecture 1
      [Introduction](https://www.youtube.com/watch?v=W1S-HSakPTM)
      Pieter Abbeel

      Lecture 2
      [Uninformed Search](https://www.youtube.com/watch?v=bSv4CWMTeR0)
      Pieter Abbeel

      Lecture 3
      [Informed Search](https://www.youtube.com/watch?v=8pTjoFiICg8)
      Pieter Abbeel

      Lecture 4
      [Constraint Satisfaction Problems I](https://www.youtube.com/watch?v=Hv_JlWld9iQ)
      Pieter Abbeel
      Recording is a bit flaky, see Fall 2013 Lecture 4 for alternative

      Lecture 5
      [Constraint Satisfaction Problems II](https://www.youtube.com/watch?v=kYVSOLX_t84)
      Pieter Abbeel

      Lecture 6
      [Adversarial Search](https://www.youtube.com/watch?v=1KTCoQRWKOE)
      Pieter Abbeel

      Lecture 7
      [Expectimax and Utilities](https://www.youtube.com/watch?v=jaFRyzp7yWw)
      Pieter Abbeel

      Lecture 8
      [Markov Decision Processes I](https://www.youtube.com/watch?v=Oxqwwnm_x0s)
      Pieter Abbeel

      Lecture 9
      [Markov Decision Processes II](https://www.youtube.com/watch?v=6pBvbLyn6fE)
      Pieter Abbeel

      Lecture 10
      [Reinforcement Learning I](https://www.youtube.com/watch?v=IXuHxkpO5E8)
      Pieter Abbeel

      Lecture 11
      [Reinforcement Learning II](https://www.youtube.com/watch?v=yNeSFbE1jdY)
      Pieter Abbeel

      Lecture 12
      [Probability](https://www.youtube.com/watch?v=cFtXkaLog5A)
      Pieter Abbeel

      Lecture 13
      [Markov Models](https://www.youtube.com/watch?v=Nxkapm7QlNw)
      Pieter Abbeel

      Lecture 14
      [Hidden Markov Models](https://www.youtube.com/watch?v=XFgdo4czYs4)
      Pieter Abbeel
      Recording is a bit flaky, see Fall 2013 Lecture 18 for alternative

      Lecture 15
      [Applications of HMMs / Speech](https://www.youtube.com/watch?v=KBg97801U40)
      Pieter Abbeel

      Lecture 16
      [Bayes' Nets: Representation](https://www.youtube.com/watch?v=gMQZq2O8yDA)
      Pieter Abbeel

      Lecture 17
      [Bayes' Nets: Independence](https://www.youtube.com/watch?v=9OajWBYRhRU)
      Pieter Abbeel

      Lecture 18
      [Bayes' Nets: Inference](https://www.youtube.com/watch?v=y9jdsPNsU_Q)
      Pieter Abbeel

      Lecture 19
      Bayes' Nets: Sampling
      Pieter Abbeel
      Unrecorded, see Fall 2013 Lecture 16

      Lecture 20
      [Decision Diagrams / Value of Perfect Information](https://www.youtube.com/watch?v=VL_IAzfC2uk)
      Pieter Abbeel

      Lecture 21
      [Machine Learning: Naive Bayes](https://www.youtube.com/watch?v=_pe60buCnxE)
      Nicholas Hay

      Lecture 22
      [Machine Learning: Perceptrons](https://www.youtube.com/watch?v=HEFfs1KCph4)
      Pieter Abbeel

      Lecture 23
      [Machine Learning: Kernels and Clustering](https://www.youtube.com/watch?v=H9DUTH9lCfg)
      Pieter Abbeel

      Lecture 24
      [Advanced Applications: NLP, Games, and Robotic Cars](https://www.youtube.com/watch?v=sMKptCC-x_o)
      Pieter Abbeel

      Lecture 25
      [Advanced Applications: Computer Vision and Robotics](https://www.youtube.com/watch?v=WZqtZ8RDVs8)
      Pieter Abbeel

      Lecture 26
      Conclusion
      Pieter Abbeel
      Unrecorded

******************

Fall 2013

    **Lecture Title****Lecturer**Notes
    
      Lecture 1
      [Introduction](https://www.youtube.com/watch?v=tONNlv6osG4)
      Dan Klein

      Lecture 2
      [Uninformed Search](https://www.youtube.com/watch?v=y9YtT49fFlM)
      Dan Klein

      Lecture 3
      [Informed Search](https://www.youtube.com/watch?v=ka5KpaKDGF0)
      Dan Klein

      Lecture 4
      [Constraint Satisfaction Problems I](https://www.youtube.com/watch?v=hJ9WOiueJes)
      Dan Klein

      Lecture 5
      [Constraint Satisfaction Problems II](https://www.youtube.com/watch?v=2TDQ_mesnOQ)
      Dan Klein

      Lecture 6
      [Adversarial Search](https://www.youtube.com/watch?v=-Il2oJoItaI)
      Dan Klein

      Lecture 7
      [Expectimax and Utilities](https://www.youtube.com/watch?v=M98BM_yJPNw)
      Dan Klein

      Lecture 8
      [Markov Decision Processes I](https://www.youtube.com/watch?v=ip4iSMRW5X4)
      Dan Klein

      Lecture 9
      [Markov Decision Processes II](https://www.youtube.com/watch?v=1S-dw6Vt1l4)
      Dan Klein

      Lecture 10
      [Reinforcement Learning I](https://www.youtube.com/watch?v=w33Lplx49_A)
      Dan Klein

      Lecture 11
      [Reinforcement Learning II](https://www.youtube.com/watch?v=jUoZg513cdE)
      Dan Klein

      Lecture 12
      [Probability](https://www.youtube.com/watch?v=8rnIArZS9cI)
      Pieter Abbeel

      Lecture 13
      [Bayes' Nets: Representation](https://www.youtube.com/watch?v=VfyxPtlqZh4)
      Pieter Abbeel

      Lecture 14
      [Bayes' Nets: Independence](https://www.youtube.com/watch?v=iaY3isLZUGs)
      Dan Klein

      Lecture 15
      [Bayes' Nets: Inference](https://www.youtube.com/watch?v=oYKAfYFmsoM)
      Pieter Abbeel

      Lecture 16
      [Bayes' Nets: Sampling](https://www.youtube.com/watch?v=ogYaTTRguIw)
      Pieter Abbeel

      Lecture 17
      [Decision Diagrams / Value of Perfect Information](https://www.youtube.com/watch?v=qQXmaWzmYuA)
      Pieter Abbeel

      Lecture 18
      [Hidden Markov Models](https://www.youtube.com/watch?v=OdQoRGRPmj8)
      Dan Klein

      Lecture 19
      [Applications of HMMs / Speech](https://www.youtube.com/watch?v=e_nQkLZens8)
      Dan Klein

      Lecture 20
      [Machine Learning: Naive Bayes](https://www.youtube.com/watch?v=iA2nEXanP_o)
      Dan Klein

      Lecture 21
      [Machine Learning: Perceptrons](https://www.youtube.com/watch?v=dXuNAkHsos4)
      Dan Klein

      Lecture 22
      [Machine Learning: Kernels and Clustering](https://www.youtube.com/watch?v=eXEi46V12dA)
      Pieter Abbeel

      Lecture 23
      [Machine Learning: Decision Trees and Neural Nets](https://www.youtube.com/watch?v=WC_DhP3vyy8)
      Pieter Abbeel

      Lecture 24
      Advanced Applications: NLP and Robotic Cars
      Dan Klein
      Unrecorded, see Spring 2013 Lecture 24

      Lecture 25
      [Advanced Applications: Computer Vision and Robotics](https://www.youtube.com/watch?v=OVgJPRSET30)
      Pieter Abbeel

      Lecture 26
      Conclusion
      Dan Klein,

Pieter Abbeel Unrecorded

******************

Spring 2013

    **Lecture Title****Lecturer**Notes
    
      Lecture 1
      Introduction
      Pieter Abbeel
      Video Down

      Lecture 2
      [Uninformed Search](https://www.youtube.com/watch?v=ST--VJJJqoc)
      Pieter Abbeel

      Lecture 3
      [Informed Search](https://www.youtube.com/watch?v=_oaqK0G_qKk)
      Pieter Abbeel

      Lecture 4
      [Constraint Satisfaction Problems I](https://www.youtube.com/watch?v=IbWL7CB15vw)
      Pieter Abbeel

      Lecture 5
      Constraint Satisfaction Problems II
      Pieter Abbeel
      Unrecorded, see Fall 2012 Lecture 5

      Lecture 6
      [Adversarial Search](https://www.youtube.com/watch?v=Nwl3LuG-fs8)
      Pieter Abbeel

      Lecture 7
      [Expectimax and Utilities](https://www.youtube.com/watch?v=r-RxPnnp__o)
      Pieter Abbeel

      Lecture 8
      [Markov Decision Processes I](https://www.youtube.com/watch?v=i0o-ui1N35U)
      Pieter Abbeel

      Lecture 9
      [Markov Decision Processes II](https://www.youtube.com/watch?v=Csiiv6WGzKM)
      Pieter Abbeel

      Lecture 10
      [Reinforcement Learning I](https://www.youtube.com/watch?v=ifma8G7LegE)
      Pieter Abbeel

      Lecture 11
      [Reinforcement Learning II](https://www.youtube.com/watch?v=Si1_YTw960c)
      Pieter Abbeel

      Lecture 12
      [Probability](https://www.youtube.com/watch?v=yRG6np3yC7c)
      Pieter Abbeel

      Lecture 13
      [Bayes' Nets: Representation](https://www.youtube.com/watch?v=uicA_yXW9LE)
      Pieter Abbeel

      Lecture 14
      [Bayes' Nets: Independence](https://www.youtube.com/watch?v=Mg3nFk3hkDE)
      Pieter Abbeel

      Lecture 15
      [Bayes' Nets: Inference](https://www.youtube.com/watch?v=_haafsCKsmY)
      Pieter Abbeel

      Lecture 16
      [Bayes' Nets: Sampling](https://www.youtube.com/watch?v=IqGbgfkkcsY)
      Pieter Abbeel

      Lecture 17
      [Decision Diagrams / Value of Perfect Information](https://www.youtube.com/watch?v=xia9VhcrMW0)
      Pieter Abbeel

      Lecture 18
      [Hidden Markov Models](https://www.youtube.com/watch?v=9dp4whVQv5s)
      Pieter Abbeel

      Lecture 19
      [Applications of HMMs / Speech](https://www.youtube.com/watch?v=Qh2hFKHOwPU)
      Pieter Abbeel

      Lecture 20
      [Machine Learning: Naive Bayes](https://www.youtube.com/watch?v=DNvwfNEiKvw)
      Pieter Abbeel

      Lecture 21
      [Machine Learning: Perceptrons I](https://www.youtube.com/watch?v=dyDdNtr9Q48)
      Nicholas Hay

      Lecture 22
      [Machine Learning: Perceptrons II](https://www.youtube.com/watch?v=rlAGutu67T0)
      Pieter Abbeel

      Lecture 23
      [Machine Learning: Kernels and Clustering](https://www.youtube.com/watch?v=ZSxvr282gBw)
      Pieter Abbeel

      Lecture 24
      [Advanced Applications: NLP and Robotic Cars](https://www.youtube.com/watch?v=AMcSRTsC15M)
      Pieter Abbeel

      Lecture 25
      [Advanced Applications: Computer Vision and Robotics](https://www.youtube.com/watch?v=MeFpNzqjkQw)
      Pieter Abbeel

      Lecture 26
      Conclusion
      Pieter Abbeel
      Unrecorded

******************

Fall 2012

    **Lecture Title****Lecturer**Notes
    
      Lecture 1
      [Introduction](https://www.youtube.com/watch?feature=player_embedded&v=J6PBD-wNEDs)
      Dan Klein

      Lecture 2
      [Uninformed Search](https://www.youtube.com/watch?feature=player_embedded&v=afwPe_OqPX0)
      Dan Klein

      Lecture 3
      [Informed Search](https://www.youtube.com/watch?feature=player_embedded&v=OGcG4jSKOVA)
      Dan Klein

      Lecture 4
      [Constraint Satisfaction Problems I](https://www.youtube.com/watch?feature=player_embedded&v=hegL0V4ckco)
      Dan Klein

      Lecture 5
      [Constraint Satisfaction Problems II](https://www.youtube.com/watch?feature=player_embedded&v=LajAWn51HmE)
      Dan Klein

      Lecture 6
      [Adversarial Search](https://www.youtube.com/watch?feature=player_embedded&v=cwbjLIahbv8)
      Dan Klein

      Lecture 7
      [Expectimax and Utilities](https://www.youtube.com/watch?feature=player_embedded&v=GevK0-9n24g)
      Dan Klein

      Lecture 8
      [Markov Decision Processes I](https://www.youtube.com/watch?feature=player_embedded&v=wKx4MuLfe0M)
      Dan Klein

      Lecture 9
      [Markov Decision Processes II](https://www.youtube.com/watch?feature=player_embedded&v=2M7mv4-BPCg)
      Dan Klein

      Lecture 10
      [Reinforcement Learning I](https://www.youtube.com/watch?feature=player_embedded&v=hsz0zq6AXGE)
      Dan Klein

      Lecture 11
      [Reinforcement Learning II](https://www.youtube.com/watch?feature=player_embedded&v=R0vTZp0ve4s)
      Dan Klein

      Lecture 12
      [Probability](https://www.youtube.com/watch?feature=player_embedded&v=UIhbGihnZ68)
      Pieter Abbeel

      Lecture 13
      [Bayes' Nets: Representation](https://www.youtube.com/watch?feature=player_embedded&v=04CITGEvGMw0)
      Pieter Abbeel

      Lecture 14
      [Bayes' Nets: Independence](https://www.youtube.com/watch?feature=player_embedded&v=GMge4cuYwZ8)
      Pieter Abbeel

      Lecture 15
      [Bayes' Nets: Inference](https://www.youtube.com/watch?feature=player_embedded&v=GgyH6Nbjdcw)
      Pieter Abbeel

      Lecture 16
      [Bayes' Nets: Sampling](https://www.youtube.com/watch?feature=player_embedded&v=y1dpQlvJ5Ys)
      Pieter Abbeel

      Lecture 17
      [Decision Diagrams / Value of Perfect Information](https://www.youtube.com/watch?feature=player_embedded&v=gp6-CH40ao8)
      Pieter Abbeel

      Lecture 18
      [Hidden Markov Models](https://www.youtube.com/watch?feature=player_embedded&v=uSpiQFD67vQ)
      Pieter Abbeel

      Lecture 19
      [Applications of HMMs / Speech](https://www.youtube.com/watch?feature=player_embedded&v=jcBY01U9czE)
      Dan Klein

      Lecture 20
      [Machine Learning: Naive Bayes](https://www.youtube.com/watch?feature=player_embedded&v=HmCxjAeGt6U)
      Dan Klein

      Lecture 21
      [Machine Learning: Perceptrons](https://www.youtube.com/watch?feature=player_embedded&v=XP7TPZPGzuk)
      Dan Klein

      Lecture 22
      [Machine Learning: Kernels and Clustering](https://www.youtube.com/watch?feature=player_embedded&v=qJdgcwzMuB0)
      Dan Klein

      Lecture 23
      [Machine Learning: Decision Trees and Neural Nets](https://www.youtube.com/watch?feature=player_embedded&v=4LGh2HlyQZ4)
      Pieter Abbeel

      Lecture 24
      [Advanced Applications: Computer Vision and Robotics](https://www.youtube.com/watch?feature=player_embedded&v=POMk-X93Bag)
      Pieter Abbeel

      Lecture 25
      Advanced Applications: NLP and Robotic Cars
      Dan Klein,

Pieter Abbeel Unrecorded

      Lecture 26
      Conclusion
      Dan Klein,

Pieter Abbeel Unrecorded

---

Lecture Slides

Here is the complete set of lecture slides, including videos, and videos of demos run in lecture: Slides [~3 GB].

The list below contains all the lecture powerpoint slides:
  
    [Lecture 1: Introduction](http://ai.berkeley.edu/slides/Lecture%201%20--%20Introduction/SP14%20CS188%20Lecture%201%20--%20Introduction.pptx)
    [Lecture 2: Uninformed Search](http://ai.berkeley.edu/slides/Lecture%202%20--%20Uninformed%20Search/SP14%20CS188%20Lecture%202%20--%20Uninformed%20Search.pptx)
    [Lecture 3: Informed Search](http://ai.berkeley.edu/slides/Lecture%203%20--%20Informed%20Search/SP14%20CS188%20Lecture%203%20--%20Informed%20Search.pptx)
    [Lecture 4: CSPs I](http://ai.berkeley.edu/slides/Lecture%204%20--%20CSPs%20I/SP14%20CS188%20Lecture%204%20--%20CSPs%20I.pptx)
    [Lecture 5: CSPs II](http://ai.berkeley.edu/slides/Lecture%205%20--%20CSPs%20II/SP14%20CS188%20Lecture%205%20--%20CSPs%20II.pptx)
    [Lecture 6: Adversarial Search](http://ai.berkeley.edu/slides/Lecture%206%20--%20Adversarial%20Search/SP14%20CS188%20Lecture%206%20--%20Adversarial%20Search.pptx)
    [Lecture 7: Expectimax Search and Utilities](http://ai.berkeley.edu/slides/Lecture%207%20--%20Expectimax%20Search%20and%20Utilities/SP14%20CS188%20Lecture%207%20--%20Expectimax%20Search%20and%20Utilities.pptx)
    [Lecture 8: MDPs I](http://ai.berkeley.edu/slides/Lecture%208%20--%20MDPs%20I/SP14%20CS188%20Lecture%208%20--%20MDPs%20I.pptx)
    [Lecture 9: MDPs II](http://ai.berkeley.edu/slides/Lecture%209%20--%20MDPs%20II/SP14%20CS188%20Lecture%209%20--%20MDPs%20II.pptx)
    [Lecture 10: Reinforcement Learning I](http://ai.berkeley.edu/slides/Lecture%2010%20--%20Reinforcement%20Learning%20I/SP14%20CS188%20Lecture%2010%20--%20Reinforcement%20Learning%20I.pptx)
    [Lecture 11: Reinforcement Learning II](http://ai.berkeley.edu/slides/Lecture%2011%20--%20Reinforcement%20Learning%20II/SP14%20CS188%20Lecture%2011%20--%20Reinforcement%20Learning%20II.pptx)
    [Lecture 12: Probability](http://ai.berkeley.edu/slides/Lecture%2012%20--%20Probability/SP14%20CS188%20Lecture%2012%20--%20Probability.pptx)
    [Lecture 13: Markov Models](http://ai.berkeley.edu/slides/Lecture%2013%20--%20Markov%20Models/SP14%20CS188%20Lecture%2013%20--%20Markov%20Models.pptx)
    [Lecture 14: Hidden Markov Models](http://ai.berkeley.edu/slides/Lecture%2014%20--%20HMMs/SP14%20CS188%20Lecture%2014%20--%20Hidden%20Markov%20Models.pptx)
    [Lecture 15: Particle Filters and Applications of HMMs](http://ai.berkeley.edu/slides/Lecture%2015%20--%20Particle%20Filters%20and%20Applications%20of%20HMMs/SP14%20CS188%20Lecture%2015%20--%20Particle%20Filters%20and%20Applications%20of%20HMMs.pptx)
    [Lecture 16: Bayes Nets I: Representation](http://ai.berkeley.edu/slides/Lecture%2016%20--%20Bayes%20Nets%20I%20Representation/SP14%20CS188%20Lecture%2016%20--%20Bayes%20Nets.pptx)
    [Lecture 17: Bayes Nets II: Independence](http://ai.berkeley.edu/slides/Lecture%2017%20--%20Bayes%20Nets%20II%20Independence/SP14%20CS188%20Lecture%2017%20--%20Bayes%20Nets%20II%20Independence.pptx)
    [Lecture 18: Bayes Nets III: Inference](http://ai.berkeley.edu/slides/Lecture%2018%20--%20Bayes%20Nets%20III%20Inference/SP14%20cs188%20Lecture%2018%20--%20Bayes%20Nets%20III%20Inference.pptx)
    [Lecture 19: Bayes Nets IV: Sampling](http://ai.berkeley.edu/slides/Lecture%2019%20--%20Bayes%20Net%20IV%20Sampling/SP14%20CS188%20Lecture%2019%20--%20Bayes%20Nets%20IV%20Sampling.pptx)
    [Lecture 20: Decision Diagrams and VPI](http://ai.berkeley.edu/slides/Lecture%2020%20--%20Decision%20Diagrams%20and%20Value%20of%20Perfect%20Information/SP14%20CS188%20Lecture%2020%20--%20Decision%20Diagrams%20and%20VPI.pptx)
    [Lecture 21: Naive Bayes](http://ai.berkeley.edu/slides/Lecture%2021%20--%20Naive%20Bayes/SP14%20CS188%20Lecture%2021%20--%20Naive%20Bayes.pptx)
    [Lecture 22: Perceptron](http://ai.berkeley.edu/slides/Lecture%2022%20--%20Perceptron/SP14%20CS188%20Lecture%2022%20--%20Perceptron.pptx)
    [Lecture 23: Kernels and Clustering](http://ai.berkeley.edu/slides/Lecture%2023%20--%20Kernels%20and%20Clustering/SP14%20CS188%20Lecture%2023%20--%20Kernels%20and%20Clustering.pptx)
    [Lecture 24: Advanced Applications (NLP, Games, Cars)](http://ai.berkeley.edu/slides/Lecture%2024%20--%20Advanced%20Applications%20(NLP,%20Games,%20Cars)/SP14%20CS188%20Lecture%2024%20--%20Advanced%20Applications%20(NLP,%20Games,%20Cars).pptx)
    [Lecture 25: Advanced Applications (Computer Vision and Robotics)](http://ai.berkeley.edu/slides/Lecture%2025%20--%20Advanced%20Applications%20(Computer%20Vision,%20Robotics)/SP14%20CS188%20Lecture%2025%20--%20Advanced%20Applications%20(Computer%20Vision%20and%20Robotics).pptx)
    [Lecture 26: Conclusion](http://ai.berkeley.edu/slides/Lecture%2026%20--%20Conclusion/SP14%20CS188%20Lecture%2026%20--%20Conclusion.pptx)

The source files for all live in-lecture demos are being prepared from Berkeley AI for release

---

Selected Research Papers

• Latest arxiv paper submissionson AI

• Peter Norvig-Teach Yourself Programming in Ten Years

• How to do Research At the MIT AI Lab

• A Roadmap towards Machine Intelligence

• Collaborative Filtering with Recurrent Neural Networks (2016)

• Wide & Deep Learning for Recommender Systems (2016)

• Deep Collaborative Filtering via Marginalized Denoising Auto-encoder (2015)

• Nonparametric bayesian multitask collaborative filtering (2013)

• Tensorflow: Large-scale machine learning on heterogeneous distributed systems

• https://infoscience.epfl.ch/record/82802/files/rr02-46.pdf

• Theano: A CPU and GPU math expression compiler.

• Caffe: Convolutional architecture for fast feature embedding

• Chainer: A powerful, flexible and intuitive framework of neural networks

• Large Scale Distributed Deep Networks

• Large-scale video classification with convolutional neural networks

• Efficient Estimation of Word Representations in Vector Space

• Grammar as a Foreign Language

• Going Deeper with Convolutions

• ON RECTIFIED LINEAR UNITS FOR SPEECH PROCESSING

• Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups.

• Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

• google turning its lucrative web search over to AI machines

Stanford Syllabus CS 20SI: Tensorflow for Deep Learning Research

• Crowd-Based Personalized Natural Language Explanations for Recommendations

Comparative Study of Deep Learning Software Frameworks

** Reddit_ML- What Are You Reading**

• AI-Powered Social Bots(16 Jun 2017)

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The Many Tribes of Artificial Intelligence

Source:https://medium.com/intuitionmachine/infographic-best-practices-in-training-deep-learning-networks-b8a3df1db53

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The Deep Learning Roadmap

Source:https://medium.com/intuitionmachine/the-deep-learning-roadmap-f0b4cac7009a

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Best Practices for Training Deep Learning Networks

Source: https://medium.com/intuitionmachine/infographic-best-practices-in-training-deep-learning-networks-b8a3df1db53

ML/DL Cheatsheets

Neural Network Architectures

Source: http://www.asimovinstitute.org/neural-network-zoo/

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Microsoft Azure Algorithm Flowchart

Source: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-cheat-sheet

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SAS Algorithm Flowchart

Source: http://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/

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Algorithm Summary

Source: http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/

---

Source: http://thinkbigdata.in/best-known-machine-learning-algorithms-infographic/

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Algorithm Pro/Con

Source: https://blog.dataiku.com/machine-learning-explained-algorithms-are-your-friend

---

Python

---

Algorithms

Source: https://www.analyticsvidhya.com/blog/2015/09/full-cheatsheet-machine-learning-algorithms/

---

Python Basics

Source: http://datasciencefree.com/python.pdf

---

Source: https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics#gs.0x1rxEA

---

Numpy

Source: https://www.dataquest.io/blog/numpy-cheat-sheet/

---

Source: http://datasciencefree.com/numpy.pdf

---

Source: https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.Nw3V6CE

---

Source: https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/numpy/numpy.ipynb

---

Pandas

Source: http://datasciencefree.com/pandas.pdf

---

Source: https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.S4P4T=U

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Source: https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/pandas/pandas.ipynb

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Matplotlib

Source: https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet

---

Source: https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/matplotlib.ipynb

---

Scikit Learn

Source: https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet#gs.fZ2A1Jk

---

Source: http://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html

---

Source: https://github.com/rcompton/ml_cheat_sheet/blob/master/supervised_learning.ipynb

---

Tensorflow

Source: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb

---

Pytorch

Source: https://github.com/bfortuner/pytorch-cheatsheet

---

Math

Probability

Source: http://www.wzchen.com/s/probability_cheatsheet.pdf

---

Linear Algebra

Source: https://minireference.com/static/tutorials/linear_algebra_in_4_pages.pdf

---

Statistics

Source: http://web.mit.edu/~csvoss/Public/usabo/stats_handout.pdf

---

Calculus

Source: http://tutorial.math.lamar.edu/getfile.aspx?file=B,41,N

---

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