Artificial intelligence and Machine Learning

In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. Colloquially, the term "artificial intelligence" is used to describe machines that mimic "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect. In Machine Learning We feed in DATA(Input) + Output, run it on machine during training and the machine creates its own program(logic), which can be evaluated while testing.

Workshop on AI

What is Artificial Intelligence ?

According to the father of Artificial Intelligence, John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs”. Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think. AI is accomplished by studying how human brain thinks, and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems.

What is Machine Learning?

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data. The process of machine learning is similar to that of data mining. Both systems search through data to look for patterns. However, instead of extracting data for human comprehension -- as is the case in data mining applications -- machine learning uses that data to detect patterns in data and adjust program actions accordingly. Example: Facebook's News Feed uses machine learning to personalize each member's feed.

Topics to be covered in Workshop

  1. Introduction : Definition of learning systems. Goals and applications of machine learning. Aspects of developing a learning system: training data, concept representation, function approximation.
  2. Inductive Classification : The concept learning task. Concept learning as search through a hypothesis space. General-to-specific ordering of hypotheses.
  3. Decision Tree Learning: Representing concepts as decision trees. Recursive induction of decision trees. Picking the best splitting attribute: entropy and information gain. Searching for simple trees and computational complexity. Occam's razor. Overfitting, noisy data, and pruning..
  4. Ensemble Learning :Using committees of multiple hypotheses. Bagging, boosting, and DECORATE. Active learning with ensembles.
  5. Experimental Evaluation of Learning Algorithms: Measuring the accuracy of learned hypotheses. Comparing learning algorithms: cross-validation, learning curves, and statistical hypothesis testing.
  6. Computational Learning Theory: Models of learnability: learning in the limit; probably approximately correct (PAC) learning. Sample complexity: quantifying the number of examples needed to PAC learn.
  7. Rule Learning: Propositional and First-Order : Translating decision trees into rules. Heuristic rule induction using separate and conquer and information gain.
  8. Artificial Neural Networks: Neurons and biological motivation. Linear threshold units. Perceptrons: representational limitation and gradient descent training
  9. Support Vector Machines: Maximum margin linear separators. Quadractic programming solution to finding maximum margin separators. Kernels for learning non-linear functions.
  10. Bayesian Learning: Probability theory and Bayes rule. Naive Bayes learning algorithm.
  11. Instance-Based Learning: Constructing explicit generalizations versus comparing to past specific examples. k-Nearest-neighbor algorithm. Case-based learning.
  12. Text Classification: Bag of words representation. Vector space model and cosine similarity. Relevance feedback and Rocchio algorithm.
  13. Clustering and Unsupervised Learning: Learning from unclassified data. Clustering. Hierarchical Aglomerative Clustering. k-means partitional clustering.
  14. Language Learning: Classification problems in language: word-sense disambiguation, sequence labeling. Hidden Markov models (HMM's).


We will use Rstudio and Rpackage for the Practice.
We will cover one real time project using Machine learning.


The duration of this workshop will be seven days, with 7-8 hour session each day .

Certification Policy:

  • Certificate of Merit for all the workshop participants.
  • At the end of this workshop, a small competition will be organized among the participating students and winners will be awarded with a 'Certificate of Excellence'.
  • Certificate of Coordination for the coordinators of the campus workshops.


Rs. 3000/- per participant (Minimum 30-40 students can be allowed in a batch)(Without Take Away Kit)

This fee include workshop training, study material & software tool Kit, certification. Incase of without kit fees model, kit will be provided for hands-on but after the workshop it will be taken back.

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