diff --git a/README.rst b/README.rst
index ee56c8de..99f6efd0 100644
--- a/README.rst
+++ b/README.rst
@@ -14,6 +14,7 @@ Ignite is a high-level library to help with training neural networks in PyTorch.
     - `Training & Validation History`_
     - `Events & Event Handlers`_
     - `Logging`_
+    - `Metrics`_
 - `Examples`_
 - `How does this compare to Torchnet?`_
 - `Contributing`_
@@ -128,6 +129,16 @@ Ignite uses `python's standard library logging module <https://docs.python.org/2
     logger.addHandler(logging.StreamHandler())
     logger.setLevel(logging.INFO)
 
+Metrics
++++++++
+Ignite supports certain metrics which can be used to classify the performance of a given model. The metrics currently available in :code:`ignite` are:
+
+- :code:`binary_accuracy` : This takes a :code:`History` object (either :code:`training_history` or :code:`validation_history`) and an optional callable transform and computes the binary accuracy which is 1 if the values are equal or 0 otherwise. This is generally used for binary classification tasks
+- :code:`categorical_accuracy` : This is the :code:`binary_accuracy` equivalent for multi-class classification where number of classes are greater than 2.
+- :code:`top_k_categorical_accuracy` : This computes the Top K classification accuracy, which is a popular mode of evaluating models on larger datasets with higher number of classes. The semantics are similar to :code:`categorical_accuracy` except there is an additional argument for the value of :code:`k`
+- :code:`mean_squared_error` : Generally used in regression tasks, this computes the sum of squared deviations between the predicted value and the actual value for a given input datapoint. This function takes a :code:`History` object and an optional callable transform and computes the mean squared error. The square root of this gives the root mean squared error (RMSE).
+- :code:`mean_absolute_error` : This is similar to the :code:`mean_squared_error` function, but instead computes the sum of absolute deviations between the predicted value and the actual value for a given input datapoint.
+
 Examples
 ++++++++
 At present, there is an example of how to use ignite to train a digit classifier on MNIST in `examples/
diff --git a/ignite/trainer/trainer.py b/ignite/trainer/trainer.py
index 6e21dd11..b1a93246 100644
--- a/ignite/trainer/trainer.py
+++ b/ignite/trainer/trainer.py
@@ -103,6 +103,29 @@ class Trainer(object):
         self._event_handlers[event_name].append((handler, args, kwargs))
         self._logger.debug("added handler for event % ", event_name)
 
+    def on(self, event_name, *args, **kwargs):
+        """
+        Decorator shortcut for add_event_handler
+
+        Parameters
+        ----------
+        event_name: enum
+            event from ignite.trainer.TrainingEvents to attach the
+            handler to
+        args:
+            optional args to be passed to `handler`
+        kwargs:
+            optional keyword args to be passed to `handler`
+
+        Returns
+        -------
+        None
+        """
+        def decorator(f):
+            self.add_event_handler(event_name, f, *args, **kwargs)
+            return f
+        return decorator
+
     def _fire_event(self, event_name):
         if event_name in self._event_handlers.keys():
             self._logger.debug("firing handlers for event %s ", event_name)

Add annotations to attach event handlers
See #37 for details
diff --git a/tests/ignite/trainer/test_trainer.py b/tests/ignite/trainer/test_trainer.py
index 1bb8cda0..e784a897 100644
--- a/tests/ignite/trainer/test_trainer.py
+++ b/tests/ignite/trainer/test_trainer.py
@@ -410,3 +410,32 @@ def test_create_supervised():
     assert loss == approx(17.0)
     assert model.weight.data[0, 0] == approx(1.3)
     assert model.bias.data[0] == approx(0.8)
+
+
+def test_on_decorator():
+    max_epochs = 5
+    num_batches = 3
+    training_data = _create_mock_data_loader(max_epochs, num_batches)
+
+    trainer = Trainer(MagicMock(return_value=1), MagicMock())
+
+    class Counter(object):
+        def __init__(self, count=0):
+            self.count = count
+
+    started_counter = Counter()
+
+    @trainer.on(TrainingEvents.TRAINING_ITERATION_STARTED, started_counter)
+    def handle_training_iteration_started(trainer, started_counter):
+        started_counter.count += 1
+
+    completed_counter = Counter()
+
+    @trainer.on(TrainingEvents.TRAINING_ITERATION_COMPLETED, completed_counter)
+    def handle_training_iteration_completed(trainer, completed_counter):
+        completed_counter.count += 1
+
+    trainer.run(training_data, max_epochs=max_epochs)
+
+    assert started_counter.count == 15
+    assert completed_counter.count == 15

pip install -e .
parse_log_pytest
pytest
numpy mock pytest codecov pytest-cov tqdm scikit-learn visdom torchvision tensorboardX gym
apt-get update apt-get install -y gcc
3.6
pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning
attrs @ file:///opt/conda/conda-bld/attrs_1642510447205/work
certifi==2021.5.30
charset-normalizer==2.0.12
cloudpickle==2.2.1
codecov==2.1.13
coverage==6.2
dataclasses==0.8
decorator==4.4.2
enum34==1.1.10
gym==0.26.2
gym-notices==0.0.8
idna==3.10
-e git+https://github.com/pytorch/ignite.git@7e6a36ed94cb87b3ee26d46a563141a2dfc1bd43#egg=ignite
importlib-metadata @ file:///tmp/build/80754af9/importlib-metadata_1631916693255/work
importlib-resources==5.4.0
iniconfig @ file:///home/linux1/recipes/ci/iniconfig_1610983019677/work
joblib==1.1.1
jsonpatch==1.32
jsonpointer==2.3
mock==5.2.0
more-itertools @ file:///tmp/build/80754af9/more-itertools_1637733554872/work
networkx==2.5.1
numpy==1.19.5
packaging @ file:///tmp/build/80754af9/packaging_1637314298585/work
Pillow==8.4.0
pluggy @ file:///tmp/build/80754af9/pluggy_1615976315926/work
protobuf==4.21.0
py @ file:///opt/conda/conda-bld/py_1644396412707/work
pyparsing @ file:///tmp/build/80754af9/pyparsing_1635766073266/work
pytest==6.2.4
pytest-cov==4.0.0
requests==2.27.1
scikit-learn==0.24.2
scipy==1.5.4
six==1.17.0
tensorboardX==2.6.2.2
threadpoolctl==3.1.0
toml @ file:///tmp/build/80754af9/toml_1616166611790/work
tomli==1.2.3
torch==1.10.1
torchvision==0.11.2
tornado==6.1
tqdm==4.64.1
typing_extensions @ file:///opt/conda/conda-bld/typing_extensions_1647553014482/work
urllib3==1.26.20
visdom==0.2.4
websocket-client==1.3.1
zipp @ file:///tmp/build/80754af9/zipp_1633618647012/work

name: ignite
channels:
  - defaults
  - https://repo.anaconda.com/pkgs/main
  - https://repo.anaconda.com/pkgs/r
  - conda-forge
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=5.1=1_gnu
  - attrs=21.4.0=pyhd3eb1b0_0
  - ca-certificates=2025.2.25=h06a4308_0
  - certifi=2021.5.30=py36h06a4308_0
  - importlib-metadata=4.8.1=py36h06a4308_0
  - importlib_metadata=4.8.1=hd3eb1b0_0
  - iniconfig=1.1.1=pyhd3eb1b0_0
  - ld_impl_linux-64=2.40=h12ee557_0
  - libffi=3.3=he6710b0_2
  - libgcc-ng=11.2.0=h1234567_1
  - libgomp=11.2.0=h1234567_1
  - libstdcxx-ng=11.2.0=h1234567_1
  - more-itertools=8.12.0=pyhd3eb1b0_0
  - ncurses=6.4=h6a678d5_0
  - openssl=1.1.1w=h7f8727e_0
  - packaging=21.3=pyhd3eb1b0_0
  - pip=21.2.2=py36h06a4308_0
  - pluggy=0.13.1=py36h06a4308_0
  - py=1.11.0=pyhd3eb1b0_0
  - pyparsing=3.0.4=pyhd3eb1b0_0
  - pytest=6.2.4=py36h06a4308_2
  - python=3.6.13=h12debd9_1
  - readline=8.2=h5eee18b_0
  - setuptools=58.0.4=py36h06a4308_0
  - sqlite=3.45.3=h5eee18b_0
  - tk=8.6.14=h39e8969_0
  - toml=0.10.2=pyhd3eb1b0_0
  - typing_extensions=4.1.1=pyh06a4308_0
  - wheel=0.37.1=pyhd3eb1b0_0
  - xz=5.6.4=h5eee18b_1
  - zipp=3.6.0=pyhd3eb1b0_0
  - zlib=1.2.13=h5eee18b_1
  - pip:
      - charset-normalizer==2.0.12
      - cloudpickle==2.2.1
      - codecov==2.1.13
      - coverage==6.2
      - dataclasses==0.8
      - decorator==4.4.2
      - enum34==1.1.10
      - gym==0.26.2
      - gym-notices==0.0.8
      - idna==3.10
      - importlib-resources==5.4.0
      - joblib==1.1.1
      - jsonpatch==1.32
      - jsonpointer==2.3
      - mock==5.2.0
      - networkx==2.5.1
      - numpy==1.19.5
      - pillow==8.4.0
      - protobuf==4.21.0
      - pytest-cov==4.0.0
      - requests==2.27.1
      - scikit-learn==0.24.2
      - scipy==1.5.4
      - six==1.17.0
      - tensorboardx==2.6.2.2
      - threadpoolctl==3.1.0
      - tomli==1.2.3
      - torch==1.10.1
      - torchvision==0.11.2
      - tornado==6.1
      - tqdm==4.64.1
      - urllib3==1.26.20
      - visdom==0.2.4
      - websocket-client==1.3.1
prefix: /opt/conda/envs/ignite

tests/ignite/trainer/test_trainer.py::test_on_decorator
tests/ignite/trainer/test_trainer.py::test_create_supervised
tests/ignite/trainer/test_trainer.py::test_adding_handler_for_non_existent_event_throws_error tests/ignite/trainer/test_trainer.py::test_exception_handler_called_on_error tests/ignite/trainer/test_trainer.py::test_adding_multiple_event_handlers tests/ignite/trainer/test_trainer.py::test_args_and_kwargs_are_passed_to_event tests/ignite/trainer/test_trainer.py::test_current_epoch_counter_increases_every_epoch tests/ignite/trainer/test_trainer.py::test_current_iteration_counter_increases_every_iteration tests/ignite/trainer/test_trainer.py::test_current_validation_iteration_counter_increases_every_iteration tests/ignite/trainer/test_trainer.py::test_validate_is_not_called_by_default tests/ignite/trainer/test_trainer.py::test_stopping_criterion_is_max_epochs tests/ignite/trainer/test_trainer.py::test_terminate_at_end_of_epoch_stops_training tests/ignite/trainer/test_trainer.py::test_terminate_at_start_of_epoch_stops_training_after_completing_iteration tests/ignite/trainer/test_trainer.py::test_terminate_stops_training_mid_epoch tests/ignite/trainer/test_trainer.py::test_terminate_stops_trainer_when_called_during_validation tests/ignite/trainer/test_trainer.py::test_terminate_after_training_iteration_skips_validation_run tests/ignite/trainer/test_trainer.py::test_training_iteration_events_are_fired tests/ignite/trainer/test_trainer.py::test_validation_iteration_events_are_fired tests/ignite/trainer/test_trainer.py::test_validation_iteration_events_are_fired_when_validate_is_called_explicitly
README.rst ignite/trainer/trainer.py
README.rst ignite/trainer/trainer.py