Interesting

Topics

in Machine

Learning

Jaley Dholakiya

Machine Learning

● Supervised

● Learning

● UnSupervise

d

● Learning

● Semi-

Supervised

● Learning

X1 X2 Y 10 20 1 10 30 1 5 10 0 10 50 0

X1 X2 10 20 10 30 5 10 10 50

I got it !

I didn't get

It :(

Lets start Differently

What is ML?

Gradient Descent (Parent Concept)

● Y = aX + b (a,b) are unknowns

Secret of ML lies in

Gradient Descent.

Moving from universe of points

to universe of parameters.

Bored of Math?

Here comes Applications

Recent Applications

Dedicated to stoners and day

dreamers.

Google Hallucinations

Google Hallucinations

Mordvintsev, Alexander; Olah, Christophe CVPR-15

Learning Human idea of object by Eye

Gaze

Jaley Dholakiya and Srinivas Krutiventi CVPR-2016

● More the hidden

● implies

● More complex

solution GRRR. . .

AlphaGo – Monte Carlo Tree Search

Monte Carlo Tree Search

Query Time Estimator

● Will cover ONLY mathematics. (not Hive)

● Estimating query time of hive

● On abstract Level . . .

● It is again similar to learning y = ax + b as described earlier. But slightly differently.

● y= (d.j).w1 + (t/vc)w2 + t.w3 + d.w4+j.w5+ w0

● d=depth of sd tree , j= pending jobs , vc=vcores/running jobs,

● t=total cpu-time, y= estimated execution time

● w=[w0 w1 w2 w3 w4 w5] and var(w)=[0.05 0.08 0.1 0.1 0.03 0.03]

Challenges

● Lot of Noise and ambiguity

● Instantaneous Nature of solution.

● Two experiments conducted

● 1. Random Forest Based

● 2. Monte Carlo based Particle Filter

Random Forest Forest of decision Trees

Particle Filter based

World Speaks

You need to burn Like a Fire,

To glow like it

Machine Learning requires :

1. Intelligence

2. Hardwork

3. Struggle

4. More Struggle

Bullshit, You burn like Fire,

I will flow like water in rain :p, You can be forgetful, crazy,

passionate,Lost, and still learn ML.

Hmm, where was I?