Friday, January 26, 2024

MIT 6.00 コンピュータサイエンスとプログラミング秋期講座第2回

MIT 6.00 コンピュータサイエンスとプログラミング秋期講座第2回

オープンコースウエア大学名：MIT
講座名：6.00 Introduction to Computer Science and Programming

Download course material from here.

Lecture 2: Operators and operands; statements; branching, conditionals, and iteration
Instructors: Prof. Eric Grimson, Prof. John Guttag
View the complete course at: http://ocw.mit.edu/6-00F08

担当教授：Prof. Eric Grimson

全講義数：24コマ
各講義時間：60分

配信開始日：2009年8月19日
講義ビデオソース：Youtube
講義ビデオ収録時間：47分30秒
サブタイトル：有
ジャンル：　効率の良いアルゴリズム、Linear, Logarithmic, Quadratic, Exponential,　バイナリーサーチ、セレクション・ソート、バブル・ソート
ビデオ画像品質5段階評価：4
（解像度が480pあり、コンピュータ画面に表示されたソースコードを比較的はっきりと読み取ることができます。）
ビデオ音声品質5段階評価：4

MIT 6.00 コンピュータサイエンスとプログラミング秋期講座第1回

MIT 6.00 コンピュータサイエンスとプログラミング秋期講座第1回

オープンコースウエア大学名：MIT
講座名：6.00 Introduction to Computer Science and Programming

Need to edit and update
講義日：2008年10月7日（火曜日）　―　第6週・1回目

担当教授：Prof. Eric Grimson

全講義数：24コマ
各講義時間：60分

配信開始日：2009年8月19日
講義ビデオソース：Youtube
講義ビデオ収録時間：47分30秒
サブタイトル：有
ジャンル：　効率の良いアルゴリズム、Linear, Logarithmic, Quadratic, Exponential,　バイナリーサーチ、セレクション・ソート、バブル・ソート
ビデオ画像品質5段階評価：4
（解像度が480pあり、コンピュータ画面に表示されたソースコードを比較的はっきりと読み取ることができます。）
ビデオ音声品質5段階評価：4

Monday, October 2, 2023

Archived Notes from Opencourseware blog MIT

http://opencourseware.cocolog-nifty.com/blog/

ココログ

第一回ブログ

Tuesday, February 9, 2021

Adding a Public Key for SSH from Google Cloud console for BITNAMI APPS

Adding a Public Key for SSH from Google Cloud console

Setup and Install WordPress on Google Cloud Platform (Bitnami)

Create a Google Cloud Platform account
Create a new project or rename a default My New Project generated by Google Cloud
Select Bitnami WordPress from the web application catalog
Select Launch
Login the WordPress instance

Generate SSH key for SSH Tunnel PHPMYAdmin running on Google Cloud

https://www.youtube.com/watch?v=T6oXTB94gBU

Generate SSH key from Putty

Install Putty (if not installed already)

Generate Key

Private Key SSH

Save the SSH key file.

Open Google Cloud Platform Console

On Google Cloud > Compute Engine > VM Instance

Select Wordpress > Edit

Copy and paste the SSH key into a Google Cloud Platform instance

The user name associated with this SSH key has been detected and displayed.

SAVE

Copy an External IP address

Save the Google Cloud Platform setting.

Open PuTTY App

Update the Host Name (IP)

Paste the external IP address copied from GCP instance

Now, set-up Authentication Key

Private key file for authentication has been specified to the file in the D drive.

Select Tunnels for SSH Tunnel set-up

Enter Source Port Number

Destination > localhost:443

Select Data to send to the server

Session: Load, save or delete a stored session

Forwarded ports

Friday, December 4, 2020

Hosting XAMPP on Linode

Hosting XAMPP on Linode with Linode Nano

Tried with digitalocean, and it did not go well.

So I will try my luck with Linode.

Thursday, August 8, 2019

Artificial Intelligence - AI

Artificial Intelligence Graduate Certificate

Stanford CS 227

Reasoning Methods in Artificial Intelligence

Stanford CS221

CS221: Artificial Intelligence: Principles and Techniques

Summer Session

CS157 - Computational Logic

CS223A - Introduction to Robotics

AA228 Decision Making Under Uncertainty

Operations Research 11: Decision Trees & Decision Making under Uncertainty

CS224N Natural Language Processing with Deep Learning

CS224U Natural Language Understanding

CS228 Probabilistic Graphical Models: Principles and Techniques

Conditional Random Fields - Stanford University (By Daphne Koller)

CS229 Machine Learning

Actually the lecture starts at 31:00
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting.

1 an overview of the course in this introductory meeting.
2 linear regression, gradient descent, and normal equations and discusses how they relate to machine learning. 3 locally weighted regression, probabilistic interpretation and logistic regression and how it relates to machine learning. 4 Newton's method, exponential families, and generalized linear models and how they relate to machine learning. 5 generative learning algorithms and Gaussian discriminative analysis and their applications in machine learning. 6 naive Bayes, neural networks, and support vector machine. 7 optimal margin classifiers, KKT conditions, and SUM duals. 8 support vector machines, including soft margin optimization and kernels. 9 learning theory, covering bias, variance, empirical risk minimization, union bound and Hoeffding's inequalities. 10 learning theory by discussing VC dimension and model selection. 11 Bayesian statistics, regularization, digression-online learning, and the applications of machine learning algorithms. 12 unsupervised learning in the context of clustering, Jensen's inequality, mixture of Gaussians, and expectation-maximization. 13 expectation-maximization in the context of the mixture of Gaussian and naive Bayes models, as well as factor analysis and digression. 14 factor analysis and expectation-maximization steps, and continues on to discuss principal component analysis (PCA). 15 principal component analysis (PCA) and independent component analysis (ICA) in relation to unsupervised machine learning. 16 reinforcement learning, focusing particularly on MDPs, value functions, and policy and value iteration. 17 reinforcement learning, focusing particularly on continuous state MDPs, discretization, and policy and value iterations. 18 state action rewards, linear dynamical systems in the context of linear quadratic regulation, models, and the Riccati equation, and finite horizon MDPs. 19 debugging process, linear quadratic regulation, Kalmer filters, and linear quadratic Gaussian in the context of reinforcement learning. 20 POMDPs, policy search, and Pegasus in the context of reinforcement learning.

CS230 Deep Learning