#AI & #ML Lecture 14: Logistic Regression & Ensemble Learning - Bagging & Boosting - AdaBoost

#ArtificialIntelligence #MachineLearning #Software #Engineering #Course
Hello everyone. My name is Furkan Gözükara, and I am a Computer Engineer Ph.D. Assistant Professor at the Software Engineering department.

In this course, starting from ground to the advanced level Artificial Intelligence and Machine Learning course will be taught.

This course requires you to be knowing a programming language or be able to utilize an Artificial Intelligence and Machine Learning tool.

Therefore, if you want to start learning to program or develop your other Software Engineering related skills, you can watch our below full courses:

automatic subtitle timing: success

[anltrak]

In this lecture, I learned

-Training revisited
-Estimating revisited
-Smoothing
-Logistic regression
-Ensemble learning
-When does bagging work?
-Boosting
-AdaBoost justification

165050017

[simayseyrek]

Training revisited
Estimating revisited
Regularization vs prior
Smoothing
The steps of probabilistic modeling
Logistic regression
MLE logistic regression
Ensemble learning
Boosting
AdaBoost justification
Simay Seyrek, 195050001

[ramabatteekh]

Training revisited
Estimating revisited
Regularization vs prior
Smoothing
the steps of probabilistic modeling
Logistic regression
MLE logistic regression
Ensemble learning
boosting
AdaBoost justification
175050902

[ammarhanyezeldinabdelrazik]

Ammar Hany
195050902
In this lesson I learnt: Training revisited, estimating revisited, logistic regression, odds ratio, MLE LR, regression vs classification. What is the difference between joint model and conditional model? What is bagging? And when does bagging work? What is Boosting?

[sezensude]

In this lesson, "Training revisited", "Estimating revisited", "Regularization vs prior", "the steps of probabilistic modeling", "regression vs classification", "ensemble learning", "split up training data", "MLE logistic regression", We learned about "boosting", "AdaBoost justification" topics.

195050801- Sude Sezen GENGENÇ

[mehmetonurderinkok]

My points are training revisited, estimating revisited, priors, priors versus regularization, probabilistic modeling of basic steps, joint model versus conditional model, odds ratio, logistic function, logistic regression, training LR models, MLE LR, overfitting, regression versus classification and linear regression. Thanks for sharing your knowledge. Sincerely yours
Mehmet Onur Derinkok 165030003

[mahsumyigit]

In this lecture;

Logistic Regression,
Bagging,
training revisited,
Boosting,
estimating revisited,
AdaBoost,
priors,
MLE logistic regression,
regression vs classification,
split up training data,
when does bagging work,

ı learned.
mahsum yiğit
165050003

[mustafacagrpeker]

In this lesson, I learned ;

- training revisited
- estimating revisited
- priors
- MLE logistic regression
- regression vs classification
- ensemble learning
- benefits of ensemble learning
- split up training data
- bagging
- bagging concern
- when does bagging work
- boosting


Mustafa Çağrı Peker
165050008