Probabilistic Graphical Models

Coursera relaunched the Probabilistic Graphical Models by Daphne Koller

Graph Theorists should make excellent programmers because everything in data structures can be thought of through graph theory.

Basically it's like someone has a PhD in this. https://www.coursera.org/specializations/probabilistic-graphical-models

https://www.coursera.org/specializations/probabilistic-graphical-models

• Part of "Statistics with R" specialization. I can recommend it to someone who has fair knowledge of confidence intervals, hypthesis testing etc. since it does a great job in comparing classical statistical methods with their Bayesian counterparts: https://www.coursera.org/learn/bayesian/home/welcome

• Nice class that teaches you basic stuff about how MCMC works and lets you play with it in JAGS: https://www.coursera.org/learn/mcmc-bayesian-statistics/home/welcome

• This is big, quite complex specialization that teaches about graphical models that have knowledge engineering, priors and Bayesian inference as their primary ways of building and training the models. It does go over MCMC. I would not recommend this specialization to someone who wants to start learning, but someone who is fairly familiar with MCMC and variational inference would find a lot of cool stuff in PGMs that were "the best thing" before deep learning revolution: https://www.coursera.org/specializations/probabilistic-graphical-models

• Bayesian Methods for Hackers is an easy to read book (available online as a github repo with all source code) that shows some of the tricks that are extremely difficult to pull off if you are using more commonplace MLE methods. This is highly recommended: https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

Just took a look at the link, it's hard for me to judge the rigor of the courses without looking into them more closely, but the heavy emphasis on SAS is a pretty big red flag for me (that they're not focusing on modern research and techniques.)

I think if you're restricted to online-only you're better off just picking and choosing from free/cheap stats courses through Coursera, stanford online etc. https://www.coursera.org/specializations/probabilistic-graphical-models is awesome, you might want to do a more basic stats one first to get the background.

I don't think you need to study more CS if you already have a bachelors. You can't really learn deep learning through a CS qualification yet, the field is too new, you're better off self-teaching.

I recommend the Coursera class on the topic. It was great. Took it because of that lecture ;-)

I haven't taken it yet but this course has a pretty stellar rating on Coursera : https://www.coursera.org/specializations/probabilistic-graphical-models Probably a good place to start

I'm going through Coursera's Probabilistic Graphical Models currently and also going through CMU 10-708's lectures. I don't think you necessarily need those things, but you'd be wayyyyy worse off without having a good starting background.

Dr. Koller (one of the founders of Coursera) also has a MOOC on graphical models on coursera:

https://www.coursera.org/specializations/probabilistic-graphical-models

Also, for PGMs checkout Daphne Koller's Probabilistic Graphical Models. I thought her book was also good, though I think I'm in the minority on that one.

I had to get that book for one of my AI classes. It's way too dense for someone new to the topic (but it's great as a reference *after* you're already familiar with the topic).

At least the Coursera class explains things in a more beginner-friendly way imo (https://www.coursera.org/specializations/probabilistic-graphical-models).

Here are a list of non superficial online courses that I truly feel are equivalent to upper division undergraduate or graduate level difficulty, off the top of my head, most of these taught by some of the best professors you could possibly find to teach the topic:

Probability Theory: https://www.edx.org/course/probability-the-science-of-uncertainty-and-data

Optimization: https://lagunita.stanford.edu/courses/Engineering/CVX101/Winter2014/

AI overview (e.g reviews algorithms and presents some introductory overview of the field): http://ai.berkeley.edu

Machine Learning: https://www.edx.org/course/machine-learning-columbiax-csmm-102x-0

PGMs: https://www.coursera.org/specializations/probabilistic-graphical-models

Deep RL: http://rail.eecs.berkeley.edu/deeprlcourse/

Robotics (Perception/Navigation or topics relevant to CS): https://www.edx.org/course/autonomous-mobile-robots

Cryptography: https://www.coursera.org/learn/crypto

Compilers: https://lagunita.stanford.edu/courses/Engineering/Compilers/Fall2014/about

Graphics: https://www.edx.org/course/computer-graphics-uc-san-diegox-cse167x-3

There is also a ton of material you can find just by googling e.g. "berkeley 184" or "cmu 10708" but they aren't technically moocs.

[Data Science]

Buongiorno,

sono uno studende magistrale di Informatica curriculum AI. Vorrei integrare il mio piano di studi con qualche corso aggiuntivo in ambito statistica (frequentabile nella mia università oppure nei corsi di Stanford) per essere poi riciclabile come data scientist unendo competenze informatiche con quelle statistica.

Alla triennale ho frequentato il seguente corso di Probabilità e Statistica: http://magistrale.educ.di.unito.it/index.php/offerta-formativa/insegnamenti/elenco-completo/elenco-completo/scheda-insegnamento?cod=MFN0600&codA=&year=2019&orienta=Z

Che ne pensate del seguente percorso:

1. [Stanford] Machine Learning di Andrer NG
2. [Uni] Teoria dell'Informazione: http://magistrale.educ.di.unito.it/index.php/offerta-formativa/insegnamenti/elenco-completo/elenco-completo/scheda-insegnamento?cod=INF0095&codA=&year=2019&orienta=XFH
3. [Uni] Apprendimento Automatico: http://magistrale.educ.di.unito.it/index.php/offerta-formativa/insegnamenti/elenco-completo/elenco-completo/scheda-insegnamento?cod=INF0091&codA=&year=2019&orienta=XH
4. [Stanford] Statistical Learning: https://www.edx.org/course/statistical-learning
5. [Uni] Bayesian Networks: Ragionamento in presenza di incertezza. La parte metodologica copre problematiche di ragionamento probabilistico, reti bayesiane e metodi di ragionamento su reti bayesiane, modelli probabilistici temporali. Dal punto di vista sperimentale, viene poi illustrato in laboratorio l’utilizzo di tool per la modellazione e l’inferenza con le Reti Bayesiane e con altri modelli probabilistici più complessi. Diagnosi Basata su Modello. Vengono presentati i principali algoritmi di Diagnosi Basata su Modello per sistemi statici e dinamici. Collegandosi al ragionamento in presenza di incertezza, tali algoritmi vengono poi confrontati con la diagnosi probabilistica di sistemi sia statici che dinamici.
6. [Stanford] Probabilistic Graphical Models Specialization: https://www.coursera.org/specializations/probabilistic-graphical-models

Che ne pensate riguardo all'ordine di frequentazione? Mancherebbe qualcosa?

Grazie per l'attenzione