##About the event
When it comes to Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI), three aspects are crucial:
- Clarity of fundamental concepts.
- Insights and nuances when applying concepts to solve real-world problems.
- Knowledge of tools for automating ML and DL.
Anthill Inside Miniconf will provide understanding on each of these fronts.
This miniconf is a full day event consisting of:
- 3-4 talks each, on concepts, applications and tools.
- Birds of Feather (BOF) sessions on focussed topics.
We are accepting proposals for:
- 10 to 40-minute talks, explaining fundamnetal concepts in math, statistics and data science.
- 20 to 40-minute talks on case studies and lessons learned when applyng ML, DL and AI concepts in different domains / to solve diverse data-related problems.
- 10 to 20-minute talks on tools on ML and DL.
- Birds of a Feather (BOF) sessions on failure stories in ML, to what problems / use cases should you use ML and DL, chatbots.
- 3-6 hour hands-on workshops on concepts and tools.
Hands-on workshops for 30-40 participants on 25 November will help in internalizing concepts, and practical aspects of working with tools.
Workshops will be announced shortly. Workshop tickets have to be purchased separately.
##Target audience, and why you should attend this event
- ML engineers who want to learn about concepts in maths, stats and strengthen foundations.
- ML engineers wanting to learn from experiences and insights of others.
- Senior architects and decision-makers who want to quick run-through of concepts, implementation case studies, and overview of tools.
- Masters and doctoral candidates who want to bridge the gap between academia and practice.
Proposals will be shortlisted and reviewed by an editorial team consisting of practitioners from the community. Make sure your abstract contains the following information:
- Key insights you will present, or takeaways for the audience.
- Overall flow of the content.
You must submit links to videos of talks you have delivered in the past, or record and upload a two-min self-recorded video explaining what your talk is about, and why is it relevant for this event.
Also consider submitting links to the following along with your proposal:
- A detailed outline, or
- Mindmap, explaining the structure of the talk, or
- Draft slides.
##Honorarium for selected speakers; travel grants
Selected speakers and workshop instructors will receive an honorarium of Rs. 3,000 each, at the end of their talk. We do not provide free passes for speakers’ colleagues and spouses.
Travel grants are available for domestic speakers. We evaluate each case on its merits, giving preference to women, people of non-binary gender, and Africans.
If you require a grant, mention this in the field where you add your location. Anthill Inside Miniconf is funded through ticket purchases and sponsorships; travel grant budgets vary.
Anthill Inside Miniconf – 24 November, 2017.
Hands-on workshops – 25 November, 2017.
For more information about speaking, Anthill Inside, sponsorships, tickets, or any other information contact firstname.lastname@example.org or call 7676332020.
Getting started with machine learning: tools, algorithms and concepts
This workshop will serve as a starting point for beginners in machine learning. I will cover a high level overview of field of machine learning and introduction to the Python data ecosystem in machine learning. I strongly believe that the best way to learn machine learning is by building few algorithms from scratch. So we will build a supervised ML application from scratch in Python. Since ML is a very vast field, I will spend some time on study guidelines and how to approach the field.
The audience can expect to take away the following after attending the workshop,
- Understanding of big picture of machine learning
- Implementation practice which helps in knowing ‘what is happening under the hood’ of most machine learning libraries
- A whirlwind tour of Python data ecosystem APIs (Numpy, Pandas and scikit-learn)
- Practical pointers on how to structure your study of machine learning
There are many ways to approach machine learning field. We can start with knowing the tools and the APIs and then gradually approach the underhood maths. Alternatively, we can start with maths and then APIs/tools can be learnt later. The workshop objective is to cover each aspect in some detail. The outline will
we as follows,
- Introduction to Python data ecosystem: few hands on exercises on numpy and pandas to serve as warmup (~ 30 minutes)
- Introduction to machine learning: mostly plain English content, covering big picture (~ 30 minutes)
- Building a regression/classifier from scratch in Python (~ 45 - 50 minutes)
- Solving a more involved problem by using scikit-learn APIs directly (~ 30 minutes)
- Next steps, how to study and which resources can be used (~ 20 minutes)
- Summarizing what we learnt, question-answers (~ 10 minutes)
Overall, I am expecting the workshop to take 3 hours +- 15 minutes. Note that this is a beginner workshop and if you are already a practicing data scientist then most of the material will be too basic for you.
- Laptop (operating system of your choice), charged battery + charger.
- Python installed on the laptop + IDE of your choice/termincal.
- No hard choice between python 2 Vs python 3.
- Following libraries MUST be installed.
- It won’t be possible to provide installation support at the time of workshop. So all requirements should be pre-installed. Without the installations, you won’t get anything out of the workshop.
It would help if you brush up the following topics from high school. Although these are not mandatory, we will cover enough details at the time of workshop.
- Basics of derivatives and concept of maxima-minima.
- Basics of matrix and vector manipulation from linear algebra.
- You should know basic programming
- Reading and Writing files
- Flow controls (if-else)
- Looping constructs like for loop, while
- Variable assignments
- In other words, you should have programmed at least few hundred lines in any mainstream programming language.
- The implementation choice for this workshop will be Python.
I work as head of data science at onlinesales.ai, an advertising technology startup based out of Pune. I have 7+ years of experience in data science and started in the field before it was a buzzword :-P. I have built multiple products, handled consulting assignments and delivered solutions using machine learning, R and Python. I hold a Master’s degree in Operations Research from Indian Institute of Technology, Mumbai.
Though I have done similar workshops multiple times before (few links given above), I try my best to do better in each iteration :-)